GNN Discovery (Step 1)

GNN File Discovery Report

Processed 2 GNN file(s) from directory: src/gnn/examples Search pattern used: **/*.md

Overall Summary

GNN files processed: 2
Files with ModelName found: 2
Files with StateSpaceBlock found: 0
Files with Connections section found: 0
Files with processing errors: 0

Detailed File Analysis

File: `src/gnn/examples/pymdp_pomdp_agent.md`

Found Sections:

ModelName: Found: Multifactor PyMDP Agent v1
StateSpaceBlock: Not found
Connections: Not found
ModelParameters: Found (3 parameters parsed)

File: `src/gnn/examples/rxinfer_multiagent_gnn.md`

Found Sections:

ModelName: Found: Multi-agent Trajectory Planning
StateSpaceBlock: Not found
Connections: Not found
ModelParameters: Found (section present, but no parameters parsed)

Test Reports (Step 3)

self = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_A_matrix_invalid_spec>

    def test_convert_A_matrix_invalid_spec(self):
        gnn_spec_mismatch = create_basic_gnn_spec(
            obs_modality_names=["Visual"], num_obs_modalities=[2],
            hidden_state_factor_names=["Location"], num_hidden_states_factors=[3],
            A_spec={"Visual": "np.array([[0.8, 0.1], [0.1, 0.8]])"} # 2x2, but expected 2x3
        )
        converter_mismatch = GnnToPyMdpConverter(gnn_spec_mismatch)
        a_matrix_str_mismatch = converter_mismatch.convert_A_matrix()
>       self.assertTrue(any("Error processing A matrix for modality Visual" in log for log in converter_mismatch.conversion_log) or
                        any("Could not parse or validate matrix A_Visual" in log for log in converter_mismatch.conversion_log) or
                        any("shapes (2,2) and (2,3) not aligned" in log.lower() for log in converter_mismatch.conversion_log))
E       AssertionError: False is not true

src/tests/render/test_pymdp_converter.py:394: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_A_matrix_multiple_modalities_single_factor>

    def test_convert_A_matrix_multiple_modalities_single_factor(self):
        gnn_spec = create_basic_gnn_spec(
            obs_modality_names=["Visual", "Audio"], num_obs_modalities=[2, 3],
            hidden_state_factor_names=["Location"], num_hidden_states_factors=[4],
            A_spec={
                "Visual": "np.array([[0.8,0.1,0.0,0.1], [0.1,0.8,0.1,0.0]])", # 2x4
                "Audio": "np.array([[0.5,0.3,0.1,0.1], [0.3,0.4,0.2,0.1], [0.1,0.1,0.6,0.2]])" # 3x4
            }
        )
        converter = GnnToPyMdpConverter(gnn_spec)
        a_matrix_str = converter.convert_A_matrix()
    
>       self.assertIn("A_Visual = np.array([[0.8,0.1,0.0,0.1], [0.1,0.8,0.1,0.0]])", a_matrix_str)
E       AssertionError: 'A_Visual = np.array([[0.8,0.1,0.0,0.1], [0.1,0.8,0.1,0.0]])' not found in 'A_Visual = utils.norm_dist(np.ones((2, 4)))\nA_Audio = utils.norm_dist(np.ones((3, 4)))\nA = np.empty(2, dtype=object)\nA[0] = A_Visual\nA[1] = A_Audio'

src/tests/render/test_pymdp_converter.py:367: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_A_matrix_numpy_array_input>

    def test_convert_A_matrix_numpy_array_input(self):
        """Test A matrix conversion when the spec provides an actual np.ndarray."""
        gnn_spec = create_basic_gnn_spec(
            obs_modality_names=["Sensor"], num_obs_modalities=[2],
            hidden_state_factor_names=["Internal"], num_hidden_states_factors=[2],
            A_spec={"Sensor": np.array([[0.9, 0.1], [0.2, 0.8]])}
        )
        converter = GnnToPyMdpConverter(gnn_spec)
        a_matrix_str = converter.convert_A_matrix()
>       self.assertIn("A_Sensor = np.array([[0.9, 0.1], [0.2, 0.8]])", a_matrix_str)
E       AssertionError: 'A_Sensor = np.array([[0.9, 0.1], [0.2, 0.8]])' not found in 'A_Sensor = np.array([[0.9,0.1],[0.2,0.8]])\nA = np.empty(1, dtype=object)\nA[0] = A_Sensor'

src/tests/render/test_pymdp_converter.py:419: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_A_matrix_single_modality_multiple_factors>

    def test_convert_A_matrix_single_modality_multiple_factors(self):
        gnn_spec = create_basic_gnn_spec(
            obs_modality_names=["Sensor"], num_obs_modalities=[2], # 2 outcomes
            hidden_state_factor_names=["FactorA", "FactorB"], num_hidden_states_factors=[2, 3], # 2 states, 3 states
            A_spec={"Sensor": "np.random.rand(2, 2, 3)"} # Shape (num_outcomes, num_states_fA, num_states_fB)
        )
        converter = GnnToPyMdpConverter(gnn_spec)
        a_matrix_str = converter.convert_A_matrix()
    
>       self.assertIn("A_Sensor = np.random.rand(2, 2, 3)", a_matrix_str)
E       AssertionError: 'A_Sensor = np.random.rand(2, 2, 3)' not found in 'A_Sensor = utils.norm_dist(np.ones((2, 2, 3)))\nA = np.empty(1, dtype=object)\nA[0] = A_Sensor'

src/tests/render/test_pymdp_converter.py:382: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_A_matrix_single_modality_single_factor>

    def test_convert_A_matrix_single_modality_single_factor(self):
        """Test A matrix conversion for a simple case: 1 obs modality, 1 state factor."""
        gnn_spec = create_basic_gnn_spec(
            obs_modality_names=["Visual"], num_obs_modalities=[2],
            hidden_state_factor_names=["Location"], num_hidden_states_factors=[3],
            A_spec={"Visual": "np.array([[0.8, 0.1, 0.1], [0.1, 0.8, 0.1]])"} # 2 obs_outcomes x 3 states
        )
        converter = GnnToPyMdpConverter(gnn_spec)
        a_matrix_str = converter.convert_A_matrix()
    
>       self.assertIn("A_Visual = np.array([[0.8, 0.1, 0.1], [0.1, 0.8, 0.1]])", a_matrix_str)
E       AssertionError: 'A_Visual = np.array([[0.8, 0.1, 0.1], [0.1, 0.8, 0.1]])' not found in 'A_Visual = np.array([[0.8,0.1,0.1],[0.1,0.8,0.1]])\nA = np.empty(1, dtype=object)\nA[0] = A_Visual'

src/tests/render/test_pymdp_converter.py:351: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_B_matrix_invalid_spec>

    def test_convert_B_matrix_invalid_spec(self):
        gnn_spec_mismatch = create_basic_gnn_spec(
            hidden_state_factor_names=["Position"], num_hidden_states_factors=[3],
            num_control_factors=[2], control_action_names_per_factor={0: ["Stay", "Move"]},
            B_spec={"Position": "np.array([[1,0],[0,1]])"} # Incorrect shape for B
        )
        converter_mismatch = GnnToPyMdpConverter(gnn_spec_mismatch)
        b_matrix_str_mismatch = converter_mismatch.convert_B_matrix()
>       self.assertTrue(any("Error processing B matrix for factor Position" in log for log in converter_mismatch.conversion_log) or
                        any("Could not parse or validate matrix B_Position" in log for log in converter_mismatch.conversion_log))
E       AssertionError: False is not true

src/tests/render/test_pymdp_converter.py:475: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_B_matrix_multiple_factors_mixed_control>

    def test_convert_B_matrix_multiple_factors_mixed_control(self):
        gnn_spec = create_basic_gnn_spec(
            hidden_state_factor_names=["Location", "Tool"], num_hidden_states_factors=[2, 3], # Loc:2 states, Tool:3 states
            num_control_factors=[2, 0], # Location controlled (2 actions), Tool not controlled
            control_action_names_per_factor={0: ["Left", "Right"]}, # Actions for Location
            B_spec={
                "Location": "np.array([[[0.9,0.1],[0.1,0.9]], [[0.1,0.9],[0.9,0.1]]])", # 2 actions x 2 states x 2 states
            }
        )
        converter = GnnToPyMdpConverter(gnn_spec)
        b_matrix_str = converter.convert_B_matrix()
    
>       self.assertIn("B_Location = np.array([[[0.9,0.1],[0.1,0.9]], [[0.1,0.9],[0.9,0.1]]])", b_matrix_str)
E       AssertionError: 'B_Location = np.array([[[0.9,0.1],[0.1,0.9]], [[0.1,0.9],[0.9,0.1]]])' not found in 'B_Location = utils.norm_dist(np.ones(2, 2, 2))\nB_Tool = utils.norm_dist(np.eye(3)[:, :, np.newaxis])\nB = np.empty(2, dtype=object)\nB[0] = B_Location\nB[1] = B_Tool'

src/tests/render/test_pymdp_converter.py:448: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_B_matrix_single_factor_controlled>

    def test_convert_B_matrix_single_factor_controlled(self):
        gnn_spec = create_basic_gnn_spec(
            hidden_state_factor_names=["Position"], num_hidden_states_factors=[3],
            num_control_factors=[2], # 2 actions for Position factor
            control_action_names_per_factor={0: ["Stay", "Move"]},
            B_spec={"Position": "np.array([[[1,0,0],[0,1,0],[0,0,1]], [[0,1,0],[0,0,1],[1,0,0]]])"} # 2 actions x 3 states x 3 states
        )
        converter = GnnToPyMdpConverter(gnn_spec)
        b_matrix_str = converter.convert_B_matrix()
    
>       self.assertIn("B_Position = np.array([[[1,0,0],[0,1,0],[0,0,1]], [[0,1,0],[0,0,1],[1,0,0]]])", b_matrix_str)
E       AssertionError: 'B_Position = np.array([[[1,0,0],[0,1,0],[0,0,1]], [[0,1,0],[0,0,1],[1,0,0]]])' not found in 'B_Position = np.array([[[1,0,0],[0,1,0],[0,0,1]],[[0,1,0],[0,0,1],[1,0,0]]])\nB = np.empty(1, dtype=object)\nB[0] = B_Position'

src/tests/render/test_pymdp_converter.py:432: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_C_vector_invalid_spec>

    def test_convert_C_vector_invalid_spec(self):
        gnn_spec_mismatch = create_basic_gnn_spec(
            obs_modality_names=["Reward"], num_obs_modalities=[3],
            C_spec={"Reward": "np.array([0.0, 1.0])"} # Mismatched size (2 vs 3)
        )
        converter_mismatch = GnnToPyMdpConverter(gnn_spec_mismatch)
        c_str_mismatch = converter_mismatch.convert_C_vector()
>       self.assertTrue(any("Error processing C vector for modality Reward" in log for log in converter_mismatch.conversion_log))
E       AssertionError: False is not true

src/tests/render/test_pymdp_converter.py:525: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_C_vector_multiple_modalities>

    def test_convert_C_vector_multiple_modalities(self):
        gnn_spec = create_basic_gnn_spec(
            obs_modality_names=["Feedback", "Score"], num_obs_modalities=[2, 5],
            C_spec={
                "Feedback": "np.array([1.0, -1.0])",
                "Score": "np.array([0, 10, 20, 30, 0])"
            }
        )
        converter = GnnToPyMdpConverter(gnn_spec)
        c_vector_str = converter.convert_C_vector()
>       self.assertIn("C_Feedback = np.array([1.0, -1.0])", c_vector_str)
E       AssertionError: 'C_Feedback = np.array([1.0, -1.0])' not found in 'C_Feedback = np.zeros(2)\nC_Score = np.array([0,10,20,30,0])\nC = np.empty(2, dtype=object)\nC[0] = C_Feedback\nC[1] = C_Score'

src/tests/render/test_pymdp_converter.py:512: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_C_vector_single_modality>

    def test_convert_C_vector_single_modality(self):
        gnn_spec = create_basic_gnn_spec(
            obs_modality_names=["Reward"], num_obs_modalities=[3], # 3 outcomes for Reward modality
            C_spec={"Reward": "np.array([0.0, 1.0, -1.0])"}
        )
        converter = GnnToPyMdpConverter(gnn_spec)
        c_vector_str = converter.convert_C_vector()
>       self.assertIn("C_Reward = np.array([0.0, 1.0, -1.0])", c_vector_str)
E       AssertionError: 'C_Reward = np.array([0.0, 1.0, -1.0])' not found in 'C_Reward = np.zeros(3)\nC = np.empty(1, dtype=object)\nC[0] = C_Reward'

src/tests/render/test_pymdp_converter.py:498: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_D_vector_invalid_spec>

    def test_convert_D_vector_invalid_spec(self):
        gnn_spec_mismatch = create_basic_gnn_spec(
            hidden_state_factor_names=["Belief"], num_hidden_states_factors=[4],
            D_spec={"Belief": "np.array([0.1, 0.9])"} # Mismatched size (2 vs 4)
        )
        converter_mismatch = GnnToPyMdpConverter(gnn_spec_mismatch)
        d_str_mismatch = converter_mismatch.convert_D_vector()
>       self.assertTrue(any("Error processing D vector for factor Belief" in log for log in converter_mismatch.conversion_log))
E       AssertionError: False is not true

src/tests/render/test_pymdp_converter.py:573: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_D_vector_multiple_factors>

    def test_convert_D_vector_multiple_factors(self):
        gnn_spec = create_basic_gnn_spec(
            hidden_state_factor_names=["Location", "Topic"], num_hidden_states_factors=[2, 3],
            D_spec={
                "Location": "np.array([0.8, 0.2])",
                "Topic": "np.array([0.5, 0.25, 0.25])"
            }
        )
        converter = GnnToPyMdpConverter(gnn_spec)
        d_vector_str = converter.convert_D_vector()
>       self.assertIn("D_Location = np.array([0.8, 0.2])", d_vector_str)
E       AssertionError: 'D_Location = np.array([0.8, 0.2])' not found in 'D_Location = np.array([0.8,0.2])\nD_Topic = np.array([0.5,0.25,0.25])\nD = np.empty(2, dtype=object)\nD[0] = D_Location\nD[1] = D_Topic'

src/tests/render/test_pymdp_converter.py:560: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_D_vector_single_factor>

    def test_convert_D_vector_single_factor(self):
        gnn_spec = create_basic_gnn_spec(
            hidden_state_factor_names=["Belief"], num_hidden_states_factors=[4],
            D_spec={"Belief": "np.array([0.1, 0.2, 0.3, 0.4])"}
        )
        converter = GnnToPyMdpConverter(gnn_spec)
        d_vector_str = converter.convert_D_vector()
>       self.assertIn("D_Belief = np.array([0.1, 0.2, 0.3, 0.4])", d_vector_str)
E       AssertionError: 'D_Belief = np.array([0.1, 0.2, 0.3, 0.4])' not found in 'D_Belief = np.array([0.1,0.2,0.3,0.4])\nD = np.empty(1, dtype=object)\nD[0] = D_Belief'

src/tests/render/test_pymdp_converter.py:546: AssertionErrorself = <src.tests.render.test_pymdp_converter.TestMatrixConversion testMethod=test_convert_E_vector_invalid_spec>

... (file truncated, total lines: 475)

pytest_report.xml

GNN Type Checker (Step 4)

Type Check Report

GNN Type Checker Report

pymdp_pomdp_agent.md: ✅ VALID

Path: src/gnn/examples/pymdp_pomdp_agent.md

rxinfer_multiagent_gnn.md: ✅ VALID

Path: src/gnn/examples/rxinfer_multiagent_gnn.md

Checked 2 files, 2 valid, 0 invalid

{
  "type_check_results": {
    "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/pymdp_pomdp_agent.md": {
      "is_valid": true,
      "errors": [],
      "warnings": []
    },
    "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md": {
      "is_valid": true,
      "errors": [],
      "warnings": []
    }
  },
  "summary": {
    "total_files": 2,
    "valid_files": 2,
    "invalid_files": 0,
    "total_errors": 0,
    "total_warnings": 0
  }
}

type_check_data.json

Resource Estimates: resource_estimates

Images

inference_time_html.png

memory_usage_html.png

storage_requirements_html.png

Markdown Reports

resource_report.md

GNN Resource Estimation Report

Analyzed 2 files Average Memory Usage: 0.50 KB Average Inference Time: 218.62 units Average Storage: 5.29 KB

pymdp_pomdp_agent.md

Path: src/gnn/examples/pymdp_pomdp_agent.md Memory Estimate: 0.48 KB Inference Estimate: 154.07 units Storage Estimate: 3.83 KB

Model Info

variables_count: 21
edges_count: 2
time_spec: Dynamic
equation_count: 5

Complexity Metrics

state_space_complexity: 6.9658
graph_density: 0.0048
avg_in_degree: 1.0000
avg_out_degree: 1.0000
max_in_degree: 1.0000
max_out_degree: 1.0000
cyclic_complexity: 0.0000
temporal_complexity: 0.0000
equation_complexity: 8.7600
overall_complexity: 8.7413
variable_count: 21.0000
edge_count: 2.0000
total_state_space_dim: 124.0000
max_variable_dim: 27.0000

rxinfer_multiagent_gnn.md

Path: src/gnn/examples/rxinfer_multiagent_gnn.md Memory Estimate: 0.52 KB Inference Estimate: 283.16 units Storage Estimate: 6.76 KB

Model Info

variables_count: 60
edges_count: 1
time_spec: Dynamic
equation_count: 15

Complexity Metrics

state_space_complexity: 6.8202
graph_density: 0.0003
avg_in_degree: 1.0000
avg_out_degree: 1.0000
max_in_degree: 1.0000
max_out_degree: 1.0000
cyclic_complexity: 0.0000
temporal_complexity: 0.0000
equation_complexity: 3.2578
overall_complexity: 5.3649
variable_count: 60.0000
edge_count: 1.0000
total_state_space_dim: 112.0000
max_variable_dim: 16.0000

Metric Definitions

General Metrics

Memory Estimate (KB): Estimated RAM required to hold the model's variables and data structures in memory. Calculated based on variable dimensions and data types (e.g., float: 4 bytes, int: 4 bytes).
Inference Estimate (units): A relative, abstract measure of computational cost for a single inference pass. It is derived from factors like model type (Static, Dynamic, Hierarchical), the number and type of variables, the complexity of connections (edges), and the operations defined in equations. Higher values indicate a more computationally intensive model. These units are not tied to a specific hardware time (e.g., milliseconds) but allow for comparison between different GNN models.
Storage Estimate (KB): Estimated disk space required to store the model file. This includes the memory footprint of the data plus overhead for the GNN textual representation, metadata, comments, and equations.

Complexity Metrics (scores are generally relative; higher often means more complex)

state_space_complexity: Logarithmic measure of the total dimensionality of all variables (sum of the product of dimensions for each variable). Represents the model's theoretical information capacity or the size of its state space.
graph_density: Ratio of actual edges to the maximum possible edges in the model graph. A value of 0 indicates no connections, while 1 would mean a fully connected graph. Measures how interconnected the variables are.
avg_in_degree: Average number of incoming connections (edges) per variable.
avg_out_degree: Average number of outgoing connections (edges) per variable.
max_in_degree: Maximum number of incoming connections for any single variable in the model.
max_out_degree: Maximum number of outgoing connections for any single variable in the model.
cyclic_complexity: A score indicating the presence and extent of cyclic patterns or feedback loops in the graph. Approximated based on the ratio of edges to variables; higher values suggest more complex recurrent interactions.
temporal_complexity: Proportion of edges that involve time dependencies (e.g., connecting a variable at time t to one at t+1). Indicates the degree to which the model's behavior depends on past states or sequences.
equation_complexity: A measure based on the average length, number, and types of mathematical operators (e.g., +, *, log, softmax) used in the model's equations. Higher values suggest more intricate mathematical relationships between variables.
overall_complexity: A weighted composite score (typically scaled, e.g., 0-10) that combines state space size, graph structure (density, cyclicity), temporal aspects, and equation complexity to provide a single, holistic measure of the model's intricacy.

HTML Reports/Outputs

resource_report_detailed.html

View standalone: resource_report_detailed.html

JSON Files

resource_data.json

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    "inference_estimate": 154.06988264859797,
    "storage_estimate": 3.82846875,
    "flops_estimate": {
      "total_flops": 1050.0,
      "matrix_operations": 0,
      "element_operations": 0,
      "nonlinear_operations": 0
    },
    "inference_time_estimate": {
      "cpu_time_seconds": 2.1e-08,
      "cpu_time_ms": 2.1e-05,
      "cpu_time_us": 0.020999999999999998
    },
    "batched_inference_estimate": {
      "batch_1": {
        "flops": 1050.0,
        "time_seconds": 2.1e-08,
        "throughput_per_second": 47619047.61904762
      },
      "batch_8": {
        "flops": 6674.971489500035,
        "time_seconds": 1.334994297900007e-07,
        "throughput_per_second": 59925349.58826627
      },
      "batch_32": {
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        "time_seconds": 5.10365156415185e-07,
        "throughput_per_second": 62700205.13306323
      },
      "batch_128": {
        "flops": 99830.77636640746,
        "time_seconds": 1.9966155273281492e-06,
        "throughput_per_second": 64108486.710652955
      },
      "batch_512": {
        "flops": 394234.3967437306,
        "time_seconds": 7.884687934874611e-06,
        "throughput_per_second": 64935987.85760216
      }
    },
    "model_overhead": {
      "compilation_ms": 79,
      "optimization_ms": 240.5,
      "memory_overhead_kb": 2.572265625
    },
    "complexity": {
      "state_space_complexity": 6.965784284662087,
      "graph_density": 0.004761904761904762,
      "avg_in_degree": 1.0,
      "avg_out_degree": 1.0,
      "max_in_degree": 1,
      "max_out_degree": 1,
      "cyclic_complexity": 0,
      "temporal_complexity": 0.0,
      "equation_complexity": 8.76,
      "overall_complexity": 8.741273094711996,
      "variable_count": 21,
      "edge_count": 2,
      "total_state_space_dim": 124,
      "max_variable_dim": 27
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      "edges_count": 2,
      "time_spec": "Dynamic",
      "equation_count": 5
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  "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md": {
    "file": "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md",
    "model_name": "Multi-agent Trajectory Planning",
    "memory_estimate": 0.5166015625,
    "inference_estimate": 283.1611446514433,
    "storage_estimate": 6.7573515625,
    "flops_estimate": {
      "total_flops": 20.0,
      "matrix_operations": 0,
      "element_operations": 8,
      "nonlinear_operations": 0
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    "inference_time_estimate": {
      "cpu_time_seconds": 4e-10,
      "cpu_time_ms": 4.0000000000000003e-07,
      "cpu_time_us": 0.0004
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    "batched_inference_estimate": {
      "batch_1": {
        "flops": 20.0,
        "time_seconds": 4e-10,
        "throughput_per_second": 2500000000.0
      },
      "batch_8": {
        "flops": 127.14231408571496,
        "time_seconds": 2.5428462817142993e-09,
        "throughput_per_second": 3146080853.383979
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      "batch_32": {
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        "time_seconds": 9.721241074574952e-09,
        "throughput_per_second": 3291760769.48582
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      "batch_128": {
        "flops": 1901.5385974553803,
        "time_seconds": 3.8030771949107605e-08,
        "throughput_per_second": 3365695552.30928
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      "batch_512": {
        "flops": 7509.226604642487,
        "time_seconds": 1.5018453209284973e-07,
        "throughput_per_second": 3409139362.5241137
      }
    },
    "model_overhead": {
      "compilation_ms": 206,
      "optimization_ms": 1820.0,
      "memory_overhead_kb": 5.423828125
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    "complexity": {
      "state_space_complexity": 6.820178962415188,
      "graph_density": 0.0002824858757062147,
      "avg_in_degree": 1.0,
      "avg_out_degree": 1.0,
      "max_in_degree": 1,
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      "cyclic_complexity": 0,
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      "equation_complexity": 3.2577777777777777,
      "overall_complexity": 5.364897390812113,
      "variable_count": 60,
      "edge_count": 1,
      "total_state_space_dim": 112,
      "max_variable_dim": 16
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    "model_info": {
      "variables_count": 60,
      "edges_count": 1,
      "time_spec": "Dynamic",
      "equation_count": 15
    }
  }
}

resource_data.json

GNN Exports (Step 5)

Export Step Report

📤 GNN Export Step Summary

🗓️ Generated: 2025-06-06 13:41:27

⚙️ Configuration

Source Directory for GNN files: src/gnn/examples
Base Output for Exports: output/gnn_exports
Recursive Search: ✅ Enabled

📊 Export Statistics

GNN Files Found/Attempted: 2
GNN Files with Successful Exports (all selected formats): 2
GNN Files with At Least One Export Failure: 0

Exports for pymdp_pomdp_agent: pymdp_pomdp_agent

JSON Files

pymdp_pomdp_agent.json

{
  "file_path": "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/pymdp_pomdp_agent.md",
  "name": "Multifactor PyMDP Agent v1",
  "metadata": {
    "description": "This model represents a PyMDP agent with multiple observation modalities and hidden state factors.\n- Observation modalities: \"state_observation\" (3 outcomes), \"reward\" (3 outcomes), \"decision_proprioceptive\" (3 outcomes)\n- Hidden state factors: \"reward_level\" (2 states), \"decision_state\" (3 states)\n- Control: \"decision_state\" factor is controllable with 3 possible actions.\nThe parameterization is derived from a PyMDP Python script example."
  },
  "states": [
    {
      "id": "A_m0",
      "dimensions": "3,2,3,type=float",
      "original_id": "A_m0"
    },
    {
      "id": "A_m1",
      "dimensions": "3,2,3,type=float",
      "original_id": "A_m1"
    },
    {
      "id": "A_m2",
      "dimensions": "3,2,3,type=float",
      "original_id": "A_m2"
    },
    {
      "id": "B_f0",
      "dimensions": "2,2,1,type=float",
      "original_id": "B_f0"
    },
    {
      "id": "B_f1",
      "dimensions": "3,3,3,type=float",
      "original_id": "B_f1"
    },
    {
      "id": "C_m0",
      "dimensions": "3,type=float",
      "original_id": "C_m0"
    },
    {
      "id": "C_m1",
      "dimensions": "3,type=float",
      "original_id": "C_m1"
    },
    {
      "id": "C_m2",
      "dimensions": "3,type=float",
      "original_id": "C_m2"
    },
    {
      "id": "D_f0",
      "dimensions": "2,type=float",
      "original_id": "D_f0"
    },
    {
      "id": "D_f1",
      "dimensions": "3,type=float",
      "original_id": "D_f1"
    },
    {
      "id": "s_f0",
      "dimensions": "2,1,type=float",
      "original_id": "s_f0"
    },
    {
      "id": "s_f1",
      "dimensions": "3,1,type=float",
      "original_id": "s_f1"
    },
    {
      "id": "s_prime_f0",
      "dimensions": "2,1,type=float",
      "original_id": "s_prime_f0"
    },
    {
      "id": "s_prime_f1",
      "dimensions": "3,1,type=float",
      "original_id": "s_prime_f1"
    },
    {
      "id": "o_m0",
      "dimensions": "3,1,type=float",
      "original_id": "o_m0"
    },
    {
      "id": "o_m1",
      "dimensions": "3,1,type=float",
      "original_id": "o_m1"
    },
    {
      "id": "o_m2",
      "dimensions": "3,1,type=float",
      "original_id": "o_m2"
    },
    {
      "id": "u_f1",
      "dimensions": "1,type=int",
      "original_id": "u_f1"
    },
    {
      "id": "G",
      "dimensions": "1,type=float",
      "original_id": "G"
    },
    {
      "id": "t",
      "dimensions": "1,type=int",
      "original_id": "t"
    }
  ],
  "parameters": {},
  "initial_parameters": {},
  "observations": [],
  "transitions": [
    {
      "sources": [
        "D_f0",
        "D_f1"
      ],
      "operator": "-",
      "targets": [
        "s_f0",
        "s_f1"
      ],
      "attributes": {}
    },
    {
      "sources": [
        "s_f0",
        "s_f1"
      ],
      "operator": "-",
      "targets": [
        "A_m0",
        "A_m1",
        "A_m2"
      ],
      "attributes": {}
    },
    {
      "sources": [
        "A_m0",
        "A_m1",
        "A_m2"
      ],
      "operator": "-",
      "targets": [
        "o_m0",
        "o_m1",
        "o_m2"
      ],
      "attributes": {}
    },
    {
      "sources": [
        "B_f0",
        "B_f1"
      ],
      "operator": "-",
      "targets": [
        "s_prime_f0",
        "s_prime_f1"
      ],
      "attributes": {}
    },
    {
      "sources": [
        "C_m0",
        "C_m1",
        "C_m2"
      ],
      "operator": ">",
      "targets": [
        "G"
      ],
      "attributes": {}
    }
  ],
  "ontology_annotations": {
    "A_m0": "LikelihoodMatrixModality0",
    "A_m1": "LikelihoodMatrixModality1",
    "A_m2": "LikelihoodMatrixModality2",
    "B_f0": "TransitionMatrixFactor0",
    "B_f1": "TransitionMatrixFactor1",
    "C_m0": "LogPreferenceVectorModality0",
    "C_m1": "LogPreferenceVectorModality1",
    "C_m2": "LogPreferenceVectorModality2",
    "D_f0": "PriorOverHiddenStatesFactor0",
    "D_f1": "PriorOverHiddenStatesFactor1",
    "s_f0": "HiddenStateFactor0",
    "s_f1": "HiddenStateFactor1",
    "s_prime_f0": "NextHiddenStateFactor0",
    "s_prime_f1": "NextHiddenStateFactor1",
    "o_m0": "ObservationModality0",
    "o_m1": "ObservationModality1",
    "o_m2": "ObservationModality2",
    "\u03c0_f1": "PolicyVectorFactor1 # Distribution over actions for factor 1",
    "u_f1": "ActionFactor1       # Chosen action for factor 1",
    "G": "ExpectedFreeEnergy"
  },
  "equations_text": "",
  "time_info": {
    "DiscreteTime": "t",
    "ModelTimeHorizon": "Unbounded # Agent definition is generally unbounded, specific simulation runs have a horizon."
  },
  "footer_text": "",
  "signature": {},
  "raw_sections": {
    "GNNSection": "MultifactorPyMDPAgent",
    "GNNVersionAndFlags": "GNN v1",
    "ModelName": "Multifactor PyMDP Agent v1",
    "ModelAnnotation": "This model represents a PyMDP agent with multiple observation modalities and hidden state factors.\n- Observation modalities: \"state_observation\" (3 outcomes), \"reward\" (3 outcomes), \"decision_proprioceptive\" (3 outcomes)\n- Hidden state factors: \"reward_level\" (2 states), \"decision_state\" (3 states)\n- Control: \"decision_state\" factor is controllable with 3 possible actions.\nThe parameterization is derived from a PyMDP Python script example.",
    "StateSpaceBlock": "# A_matrices are defined per modality: A_m[observation_outcomes, state_factor0_states, state_factor1_states]\nA_m0[3,2,3,type=float]   # Likelihood for modality 0 (\"state_observation\")\nA_m1[3,2,3,type=float]   # Likelihood for modality 1 (\"reward\")\nA_m2[3,2,3,type=float]   # Likelihood for modality 2 (\"decision_proprioceptive\")\n\n# B_matrices are defined per hidden state factor: B_f[states_next, states_previous, actions]\nB_f0[2,2,1,type=float]   # Transitions for factor 0 (\"reward_level\"), 1 implicit action (uncontrolled)\nB_f1[3,3,3,type=float]   # Transitions for factor 1 (\"decision_state\"), 3 actions\n\n# C_vectors are defined per modality: C_m[observation_outcomes]\nC_m0[3,type=float]       # Preferences for modality 0\nC_m1[3,type=float]       # Preferences for modality 1\nC_m2[3,type=float]       # Preferences for modality 2\n\n# D_vectors are defined per hidden state factor: D_f[states]\nD_f0[2,type=float]       # Prior for factor 0\nD_f1[3,type=float]       # Prior for factor 1\n\n# Hidden States\ns_f0[2,1,type=float]     # Hidden state for factor 0 (\"reward_level\")\ns_f1[3,1,type=float]     # Hidden state for factor 1 (\"decision_state\")\ns_prime_f0[2,1,type=float] # Next hidden state for factor 0\ns_prime_f1[3,1,type=float] # Next hidden state for factor 1\n\n# Observations\no_m0[3,1,type=float]     # Observation for modality 0\no_m1[3,1,type=float]     # Observation for modality 1\no_m2[3,1,type=float]     # Observation for modality 2\n\n# Policy and Control\n\u03c0_f1[3,type=float]       # Policy (distribution over actions) for controllable factor 1\nu_f1[1,type=int]         # Action taken for controllable factor 1\nG[1,type=float]          # Expected Free Energy (overall, or can be per policy)\nt[1,type=int]            # Time step",
    "Connections": "(D_f0,D_f1)-(s_f0,s_f1)\n(s_f0,s_f1)-(A_m0,A_m1,A_m2)\n(A_m0,A_m1,A_m2)-(o_m0,o_m1,o_m2)\n(s_f0,s_f1,u_f1)-(B_f0,B_f1) # u_f1 primarily affects B_f1; B_f0 is uncontrolled\n(B_f0,B_f1)-(s_prime_f0,s_prime_f1)\n(C_m0,C_m1,C_m2)>G\nG>\u03c0_f1\n\u03c0_f1-u_f1\nG=ExpectedFreeEnergy\nt=Time",
    "InitialParameterization": "# A_m0: num_obs[0]=3, num_states[0]=2, num_states[1]=3. Format: A[obs_idx][state_f0_idx][state_f1_idx]\n# A[0][:, :, 0] = np.ones((3,2))/3\n# A[0][:, :, 1] = np.ones((3,2))/3\n# A[0][:, :, 2] = [[0.8,0.2],[0.0,0.0],[0.2,0.8]] (obs x state_f0 for state_f1=2)\nA_m0={\n  ( (0.33333,0.33333,0.8), (0.33333,0.33333,0.2) ),  # obs=0; (vals for s_f1 over s_f0=0), (vals for s_f1 over s_f0=1)\n  ( (0.33333,0.33333,0.0), (0.33333,0.33333,0.0) ),  # obs=1\n  ( (0.33333,0.33333,0.2), (0.33333,0.33333,0.8) )   # obs=2\n}\n\n# A_m1: num_obs[1]=3, num_states[0]=2, num_states[1]=3\n# A[1][2, :, 0] = [1.0,1.0]\n# A[1][0:2, :, 1] = softmax([[1,0],[0,1]]) approx [[0.731,0.269],[0.269,0.731]]\n# A[1][2, :, 2] = [1.0,1.0]\n# Others are 0.\nA_m1={\n  ( (0.0,0.731,0.0), (0.0,0.269,0.0) ),  # obs=0\n  ( (0.0,0.269,0.0), (0.0,0.731,0.0) ),  # obs=1\n  ( (1.0,0.0,1.0), (1.0,0.0,1.0) )      # obs=2\n}\n\n# A_m2: num_obs[2]=3, num_states[0]=2, num_states[1]=3\n# A[2][0,:,0]=1.0; A[2][1,:,1]=1.0; A[2][2,:,2]=1.0\n# Others are 0.\nA_m2={\n  ( (1.0,0.0,0.0), (1.0,0.0,0.0) ),  # obs=0\n  ( (0.0,1.0,0.0), (0.0,1.0,0.0) ),  # obs=1\n  ( (0.0,0.0,1.0), (0.0,0.0,1.0) )   # obs=2\n}\n\n# B_f0: factor 0 (2 states), uncontrolled (1 action). Format B[s_next, s_prev, action=0]\n# B_f0 = eye(2)\nB_f0={\n  ( (1.0),(0.0) ), # s_next=0; (vals for s_prev over action=0)\n  ( (0.0),(1.0) )  # s_next=1\n}\n\n# B_f1: factor 1 (3 states), 3 actions. Format B[s_next, s_prev, action_idx]\n# B_f1[:,:,action_idx] = eye(3) for each action\nB_f1={\n  ( (1.0,1.0,1.0), (0.0,0.0,0.0), (0.0,0.0,0.0) ), # s_next=0; (vals for actions over s_prev=0), (vals for actions over s_prev=1), ...\n  ( (0.0,0.0,0.0), (1.0,1.0,1.0), (0.0,0.0,0.0) ), # s_next=1\n  ( (0.0,0.0,0.0), (0.0,0.0,0.0), (1.0,1.0,1.0) )  # s_next=2\n}\n\n# C_m0: num_obs[0]=3. Defaults to zeros.\nC_m0={(0.0,0.0,0.0)}\n\n# C_m1: num_obs[1]=3. C[1][0]=1.0, C[1][1]=-2.0\nC_m1={(1.0,-2.0,0.0)}\n\n# C_m2: num_obs[2]=3. Defaults to zeros.\nC_m2={(0.0,0.0,0.0)}\n\n# D_f0: factor 0 (2 states). Uniform prior.\nD_f0={(0.5,0.5)}\n\n# D_f1: factor 1 (3 states). Uniform prior.\nD_f1={(0.33333,0.33333,0.33333)}",
    "InitialParameterization_raw_content": "# A_m0: num_obs[0]=3, num_states[0]=2, num_states[1]=3. Format: A[obs_idx][state_f0_idx][state_f1_idx]\n# A[0][:, :, 0] = np.ones((3,2))/3\n# A[0][:, :, 1] = np.ones((3,2))/3\n# A[0][:, :, 2] = [[0.8,0.2],[0.0,0.0],[0.2,0.8]] (obs x state_f0 for state_f1=2)\nA_m0={\n  ( (0.33333,0.33333,0.8), (0.33333,0.33333,0.2) ),  # obs=0; (vals for s_f1 over s_f0=0), (vals for s_f1 over s_f0=1)\n  ( (0.33333,0.33333,0.0), (0.33333,0.33333,0.0) ),  # obs=1\n  ( (0.33333,0.33333,0.2), (0.33333,0.33333,0.8) )   # obs=2\n}\n\n# A_m1: num_obs[1]=3, num_states[0]=2, num_states[1]=3\n# A[1][2, :, 0] = [1.0,1.0]\n# A[1][0:2, :, 1] = softmax([[1,0],[0,1]]) approx [[0.731,0.269],[0.269,0.731]]\n# A[1][2, :, 2] = [1.0,1.0]\n# Others are 0.\nA_m1={\n  ( (0.0,0.731,0.0), (0.0,0.269,0.0) ),  # obs=0\n  ( (0.0,0.269,0.0), (0.0,0.731,0.0) ),  # obs=1\n  ( (1.0,0.0,1.0), (1.0,0.0,1.0) )      # obs=2\n}\n\n# A_m2: num_obs[2]=3, num_states[0]=2, num_states[1]=3\n# A[2][0,:,0]=1.0; A[2][1,:,1]=1.0; A[2][2,:,2]=1.0\n# Others are 0.\nA_m2={\n  ( (1.0,0.0,0.0), (1.0,0.0,0.0) ),  # obs=0\n  ( (0.0,1.0,0.0), (0.0,1.0,0.0) ),  # obs=1\n  ( (0.0,0.0,1.0), (0.0,0.0,1.0) )   # obs=2\n}\n\n# B_f0: factor 0 (2 states), uncontrolled (1 action). Format B[s_next, s_prev, action=0]\n# B_f0 = eye(2)\nB_f0={\n  ( (1.0),(0.0) ), # s_next=0; (vals for s_prev over action=0)\n  ( (0.0),(1.0) )  # s_next=1\n}\n\n# B_f1: factor 1 (3 states), 3 actions. Format B[s_next, s_prev, action_idx]\n# B_f1[:,:,action_idx] = eye(3) for each action\nB_f1={\n  ( (1.0,1.0,1.0), (0.0,0.0,0.0), (0.0,0.0,0.0) ), # s_next=0; (vals for actions over s_prev=0), (vals for actions over s_prev=1), ...\n  ( (0.0,0.0,0.0), (1.0,1.0,1.0), (0.0,0.0,0.0) ), # s_next=1\n  ( (0.0,0.0,0.0), (0.0,0.0,0.0), (1.0,1.0,1.0) )  # s_next=2\n}\n\n# C_m0: num_obs[0]=3. Defaults to zeros.\nC_m0={(0.0,0.0,0.0)}\n\n# C_m1: num_obs[1]=3. C[1][0]=1.0, C[1][1]=-2.0\nC_m1={(1.0,-2.0,0.0)}\n\n# C_m2: num_obs[2]=3. Defaults to zeros.\nC_m2={(0.0,0.0,0.0)}\n\n# D_f0: factor 0 (2 states). Uniform prior.\nD_f0={(0.5,0.5)}\n\n# D_f1: factor 1 (3 states). Uniform prior.\nD_f1={(0.33333,0.33333,0.33333)}",
    "Equations": "# Standard PyMDP agent equations for state inference (infer_states),\n# policy inference (infer_policies), and action sampling (sample_action).\n# qs = infer_states(o)\n# q_pi, efe = infer_policies()\n# action = sample_action()",
    "Time": "Dynamic\nDiscreteTime=t\nModelTimeHorizon=Unbounded # Agent definition is generally unbounded, specific simulation runs have a horizon.",
    "ActInfOntologyAnnotation": "A_m0=LikelihoodMatrixModality0\nA_m1=LikelihoodMatrixModality1\nA_m2=LikelihoodMatrixModality2\nB_f0=TransitionMatrixFactor0\nB_f1=TransitionMatrixFactor1\nC_m0=LogPreferenceVectorModality0\nC_m1=LogPreferenceVectorModality1\nC_m2=LogPreferenceVectorModality2\nD_f0=PriorOverHiddenStatesFactor0\nD_f1=PriorOverHiddenStatesFactor1\ns_f0=HiddenStateFactor0\ns_f1=HiddenStateFactor1\ns_prime_f0=NextHiddenStateFactor0\ns_prime_f1=NextHiddenStateFactor1\no_m0=ObservationModality0\no_m1=ObservationModality1\no_m2=ObservationModality2\n\u03c0_f1=PolicyVectorFactor1 # Distribution over actions for factor 1\nu_f1=ActionFactor1       # Chosen action for factor 1\nG=ExpectedFreeEnergy",
    "ModelParameters": "num_hidden_states_factors: [2, 3]  # s_f0[2], s_f1[3]\nnum_obs_modalities: [3, 3, 3]     # o_m0[3], o_m1[3], o_m2[3]\nnum_control_factors: [1, 3]   # B_f0 actions_dim=1 (uncontrolled), B_f1 actions_dim=3 (controlled by pi_f1)",
    "Footer": "Multifactor PyMDP Agent v1 - GNN Representation",
    "Signature": "NA"
  },
  "other_sections": {},
  "gnnsection": {},
  "gnnversionandflags": {},
  "equations": "# Standard PyMDP agent equations for state inference (infer_states),\n# policy inference (infer_policies), and action sampling (sample_action).\n# qs = infer_states(o)\n# q_pi, efe = infer_policies()\n# action = sample_action()",
  "ModelParameters": {
    "num_hidden_states_factors": "[2, 3]",
    "num_obs_modalities": "[3, 3, 3]",
    "num_control_factors": "[1, 3]"
  },
  "num_hidden_states_factors": "[2, 3]",
  "num_obs_modalities": "[3, 3, 3]",
  "num_control_factors": "[1, 3]",
  "footer": "Multifactor PyMDP Agent v1 - GNN Representation"
}

pymdp_pomdp_agent.json

Text/Log Files

pymdp_pomdp_agent.txt

GNN Model Summary: Multifactor PyMDP Agent v1
Source File: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/pymdp_pomdp_agent.md

Metadata:
  description: This model represents a PyMDP agent with multiple observation modalities and hidden state factors.
- Observation modalities: "state_observation" (3 outcomes), "reward" (3 outcomes), "decision_proprioceptive" (3 outcomes)
- Hidden state factors: "reward_level" (2 states), "decision_state" (3 states)
- Control: "decision_state" factor is controllable with 3 possible actions.
The parameterization is derived from a PyMDP Python script example.

States (20):
  - ID: A_m0 (dimensions=3,2,3,type=float, original_id=A_m0)
  - ID: A_m1 (dimensions=3,2,3,type=float, original_id=A_m1)
  - ID: A_m2 (dimensions=3,2,3,type=float, original_id=A_m2)
  - ID: B_f0 (dimensions=2,2,1,type=float, original_id=B_f0)
  - ID: B_f1 (dimensions=3,3,3,type=float, original_id=B_f1)
  - ID: C_m0 (dimensions=3,type=float, original_id=C_m0)
  - ID: C_m1 (dimensions=3,type=float, original_id=C_m1)
  - ID: C_m2 (dimensions=3,type=float, original_id=C_m2)
  - ID: D_f0 (dimensions=2,type=float, original_id=D_f0)
  - ID: D_f1 (dimensions=3,type=float, original_id=D_f1)
  - ID: s_f0 (dimensions=2,1,type=float, original_id=s_f0)
  - ID: s_f1 (dimensions=3,1,type=float, original_id=s_f1)
  - ID: s_prime_f0 (dimensions=2,1,type=float, original_id=s_prime_f0)
  - ID: s_prime_f1 (dimensions=3,1,type=float, original_id=s_prime_f1)
  - ID: o_m0 (dimensions=3,1,type=float, original_id=o_m0)
  - ID: o_m1 (dimensions=3,1,type=float, original_id=o_m1)
  - ID: o_m2 (dimensions=3,1,type=float, original_id=o_m2)
  - ID: u_f1 (dimensions=1,type=int, original_id=u_f1)
  - ID: G (dimensions=1,type=float, original_id=G)
  - ID: t (dimensions=1,type=int, original_id=t)

Initial Parameters (0):

General Parameters (0):

Observations (0):

Transitions (5):
  - None -> None
  - None -> None
  - None -> None
  - None -> None
  - None -> None

Ontology Annotations (20):
  A_m0 = LikelihoodMatrixModality0
  A_m1 = LikelihoodMatrixModality1
  A_m2 = LikelihoodMatrixModality2
  B_f0 = TransitionMatrixFactor0
  B_f1 = TransitionMatrixFactor1
  C_m0 = LogPreferenceVectorModality0
  C_m1 = LogPreferenceVectorModality1
  C_m2 = LogPreferenceVectorModality2
  D_f0 = PriorOverHiddenStatesFactor0
  D_f1 = PriorOverHiddenStatesFactor1
  s_f0 = HiddenStateFactor0
  s_f1 = HiddenStateFactor1
  s_prime_f0 = NextHiddenStateFactor0
  s_prime_f1 = NextHiddenStateFactor1
  o_m0 = ObservationModality0
  o_m1 = ObservationModality1
  o_m2 = ObservationModality2
  π_f1 = PolicyVectorFactor1 # Distribution over actions for factor 1
  u_f1 = ActionFactor1       # Chosen action for factor 1
  G = ExpectedFreeEnergy

pymdp_pomdp_agent.txt

Other Files

Exports for rxinfer_multiagent_gnn: rxinfer_multiagent_gnn

JSON Files

rxinfer_multiagent_gnn.json

{
  "file_path": "/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md",
  "name": "Multi-agent Trajectory Planning",
  "metadata": {
    "description": "This model represents a multi-agent trajectory planning scenario in RxInfer.jl.\nIt includes:\n- State space model for agents moving in a 2D environment\n- Obstacle avoidance constraints\n- Goal-directed behavior\n- Inter-agent collision avoidance\nThe model can be used to simulate trajectory planning in various environments with obstacles."
  },
  "states": [
    {
      "id": "dt",
      "dimensions": "1,type=float",
      "original_id": "dt"
    },
    {
      "id": "gamma",
      "dimensions": "1,type=float",
      "original_id": "gamma"
    },
    {
      "id": "nr_steps",
      "dimensions": "1,type=int",
      "original_id": "nr_steps"
    },
    {
      "id": "nr_iterations",
      "dimensions": "1,type=int",
      "original_id": "nr_iterations"
    },
    {
      "id": "nr_agents",
      "dimensions": "1,type=int",
      "original_id": "nr_agents"
    },
    {
      "id": "softmin_temperature",
      "dimensions": "1,type=float",
      "original_id": "softmin_temperature"
    },
    {
      "id": "intermediate_steps",
      "dimensions": "1,type=int",
      "original_id": "intermediate_steps"
    },
    {
      "id": "save_intermediates",
      "dimensions": "1,type=bool",
      "original_id": "save_intermediates"
    },
    {
      "id": "A",
      "dimensions": "4,4,type=float",
      "original_id": "A"
    },
    {
      "id": "B",
      "dimensions": "4,2,type=float",
      "original_id": "B"
    },
    {
      "id": "C",
      "dimensions": "2,4,type=float",
      "original_id": "C"
    },
    {
      "id": "initial_state_variance",
      "dimensions": "1,type=float",
      "original_id": "initial_state_variance"
    },
    {
      "id": "control_variance",
      "dimensions": "1,type=float",
      "original_id": "control_variance"
    },
    {
      "id": "goal_constraint_variance",
      "dimensions": "1,type=float",
      "original_id": "goal_constraint_variance"
    },
    {
      "id": "gamma_shape",
      "dimensions": "1,type=float",
      "original_id": "gamma_shape"
    },
    {
      "id": "gamma_scale_factor",
      "dimensions": "1,type=float",
      "original_id": "gamma_scale_factor"
    },
    {
      "id": "x_limits",
      "dimensions": "2,type=float",
      "original_id": "x_limits"
    },
    {
      "id": "y_limits",
      "dimensions": "2,type=float",
      "original_id": "y_limits"
    },
    {
      "id": "fps",
      "dimensions": "1,type=int",
      "original_id": "fps"
    },
    {
      "id": "heatmap_resolution",
      "dimensions": "1,type=int",
      "original_id": "heatmap_resolution"
    },
    {
      "id": "plot_width",
      "dimensions": "1,type=int",
      "original_id": "plot_width"
    },
    {
      "id": "plot_height",
      "dimensions": "1,type=int",
      "original_id": "plot_height"
    },
    {
      "id": "agent_alpha",
      "dimensions": "1,type=float",
      "original_id": "agent_alpha"
    },
    {
      "id": "target_alpha",
      "dimensions": "1,type=float",
      "original_id": "target_alpha"
    },
    {
      "id": "color_palette",
      "dimensions": "1,type=string",
      "original_id": "color_palette"
    },
    {
      "id": "door_obstacle_center_1",
      "dimensions": "2,type=float",
      "original_id": "door_obstacle_center_1"
    },
    {
      "id": "door_obstacle_size_1",
      "dimensions": "2,type=float",
      "original_id": "door_obstacle_size_1"
    },
    {
      "id": "door_obstacle_center_2",
      "dimensions": "2,type=float",
      "original_id": "door_obstacle_center_2"
    },
    {
      "id": "door_obstacle_size_2",
      "dimensions": "2,type=float",
      "original_id": "door_obstacle_size_2"
    },
    {
      "id": "wall_obstacle_center",
      "dimensions": "2,type=float",
      "original_id": "wall_obstacle_center"
    },
    {
      "id": "wall_obstacle_size",
      "dimensions": "2,type=float",
      "original_id": "wall_obstacle_size"
    },
    {
      "id": "combined_obstacle_center_1",
      "dimensions": "2,type=float",
      "original_id": "combined_obstacle_center_1"
    },
    {
      "id": "combined_obstacle_size_1",
      "dimensions": "2,type=float",
      "original_id": "combined_obstacle_size_1"
    },
    {
      "id": "combined_obstacle_center_2",
      "dimensions": "2,type=float",
      "original_id": "combined_obstacle_center_2"
    },
    {
      "id": "combined_obstacle_size_2",
      "dimensions": "2,type=float",
      "original_id": "combined_obstacle_size_2"
    },
    {
      "id": "combined_obstacle_center_3",
      "dimensions": "2,type=float",
      "original_id": "combined_obstacle_center_3"
    },
    {
      "id": "combined_obstacle_size_3",
      "dimensions": "2,type=float",
      "original_id": "combined_obstacle_size_3"
    },
    {
      "id": "agent1_id",
      "dimensions": "1,type=int",
      "original_id": "agent1_id"
    },
    {
      "id": "agent1_radius",
      "dimensions": "1,type=float",
      "original_id": "agent1_radius"
    },
    {
      "id": "agent1_initial_position",
      "dimensions": "2,type=float",
      "original_id": "agent1_initial_position"
    },
    {
      "id": "agent1_target_position",
      "dimensions": "2,type=float",
      "original_id": "agent1_target_position"
    },
    {
      "id": "agent2_id",
      "dimensions": "1,type=int",
      "original_id": "agent2_id"
    },
    {
      "id": "agent2_radius",
      "dimensions": "1,type=float",
      "original_id": "agent2_radius"
    },
    {
      "id": "agent2_initial_position",
      "dimensions": "2,type=float",
      "original_id": "agent2_initial_position"
    },
    {
      "id": "agent2_target_position",
      "dimensions": "2,type=float",
      "original_id": "agent2_target_position"
    },
    {
      "id": "agent3_id",
      "dimensions": "1,type=int",
      "original_id": "agent3_id"
    },
    {
      "id": "agent3_radius",
      "dimensions": "1,type=float",
      "original_id": "agent3_radius"
    },
    {
      "id": "agent3_initial_position",
      "dimensions": "2,type=float",
      "original_id": "agent3_initial_position"
    },
    {
      "id": "agent3_target_position",
      "dimensions": "2,type=float",
      "original_id": "agent3_target_position"
    },
    {
      "id": "agent4_id",
      "dimensions": "1,type=int",
      "original_id": "agent4_id"
    },
    {
      "id": "agent4_radius",
      "dimensions": "1,type=float",
      "original_id": "agent4_radius"
    },
    {
      "id": "agent4_initial_position",
      "dimensions": "2,type=float",
      "original_id": "agent4_initial_position"
    },
    {
      "id": "agent4_target_position",
      "dimensions": "2,type=float",
      "original_id": "agent4_target_position"
    },
    {
      "id": "experiment_seeds",
      "dimensions": "2,type=int",
      "original_id": "experiment_seeds"
    },
    {
      "id": "results_dir",
      "dimensions": "1,type=string",
      "original_id": "results_dir"
    },
    {
      "id": "animation_template",
      "dimensions": "1,type=string",
      "original_id": "animation_template"
    },
    {
      "id": "control_vis_filename",
      "dimensions": "1,type=string",
      "original_id": "control_vis_filename"
    },
    {
      "id": "obstacle_distance_filename",
      "dimensions": "1,type=string",
      "original_id": "obstacle_distance_filename"
    },
    {
      "id": "path_uncertainty_filename",
      "dimensions": "1,type=string",
      "original_id": "path_uncertainty_filename"
    },
    {
      "id": "convergence_filename",
      "dimensions": "1,type=string",
      "original_id": "convergence_filename"
    }
  ],
  "parameters": {},
  "initial_parameters": {},
  "observations": [],
  "transitions": [
    {
      "sources": [
        "dt"
      ],
      "operator": ">",
      "targets": [
        "A"
      ],
      "attributes": {}
    },
    {
      "sources": [
        "A",
        "B",
        "C"
      ],
      "operator": ">",
      "targets": [
        "state_space_model"
      ],
      "attributes": {}
    },
    {
      "sources": [
        "state_space_model",
        "initial_state_variance",
        "control_variance"
      ],
      "operator": ">",
      "targets": [
        "agent_trajectories"
      ],
      "attributes": {}
    },
    {
      "sources": [
        "agent_trajectories",
        "goal_constraint_variance"
      ],
      "operator": ">",
      "targets": [
        "goal_directed_behavior"
      ],
      "attributes": {}
    },
    {
      "sources": [
        "agent_trajectories",
        "gamma",
        "gamma_shape",
        "gamma_scale_factor"
      ],
      "operator": ">",
      "targets": [
        "obstacle_avoidance"
      ],
      "attributes": {}
    },
    {
      "sources": [
        "agent_trajectories",
        "nr_agents"
      ],
      "operator": ">",
      "targets": [
        "collision_avoidance"
      ],
      "attributes": {}
    },
    {
      "sources": [
        "goal_directed_behavior",
        "obstacle_avoidance",
        "collision_avoidance"
      ],
      "operator": ">",
      "targets": [
        "planning_system"
      ],
      "attributes": {}
    }
  ],
  "ontology_annotations": {
    "dt": "TimeStep",
    "gamma": "ConstraintParameter",
    "nr_steps": "TrajectoryLength",
    "nr_iterations": "InferenceIterations",
    "nr_agents": "NumberOfAgents",
    "softmin_temperature": "SoftminTemperature",
    "A": "StateTransitionMatrix",
    "B": "ControlInputMatrix",
    "C": "ObservationMatrix",
    "initial_state_variance": "InitialStateVariance",
    "control_variance": "ControlVariance",
    "goal_constraint_variance": "GoalConstraintVariance"
  },
  "equations_text": "",
  "time_info": {
    "ModelTimeHorizon": "nr_steps"
  },
  "footer_text": "",
  "signature": {
    "Creator": "AI Assistant for GNN",
    "Date": "2024-07-27",
    "Status": "Example for RxInfer.jl multi-agent trajectory planning"
  },
  "raw_sections": {
    "GNNSection": "RxInferMultiAgentTrajectoryPlanning",
    "GNNVersionAndFlags": "GNN v1",
    "ModelName": "Multi-agent Trajectory Planning",
    "ModelAnnotation": "This model represents a multi-agent trajectory planning scenario in RxInfer.jl.\nIt includes:\n- State space model for agents moving in a 2D environment\n- Obstacle avoidance constraints\n- Goal-directed behavior\n- Inter-agent collision avoidance\nThe model can be used to simulate trajectory planning in various environments with obstacles.",
    "StateSpaceBlock": "# Model parameters\ndt[1,type=float]               # Time step for the state space model\ngamma[1,type=float]            # Constraint parameter for the Halfspace node\nnr_steps[1,type=int]           # Number of time steps in the trajectory\nnr_iterations[1,type=int]      # Number of inference iterations\nnr_agents[1,type=int]          # Number of agents in the simulation\nsoftmin_temperature[1,type=float] # Temperature parameter for the softmin function\nintermediate_steps[1,type=int] # Intermediate results saving interval\nsave_intermediates[1,type=bool] # Whether to save intermediate results\n\n# State space matrices\nA[4,4,type=float]              # State transition matrix\nB[4,2,type=float]              # Control input matrix\nC[2,4,type=float]              # Observation matrix\n\n# Prior distributions\ninitial_state_variance[1,type=float]    # Prior on initial state\ncontrol_variance[1,type=float]          # Prior on control inputs\ngoal_constraint_variance[1,type=float]  # Goal constraints variance\ngamma_shape[1,type=float]               # Parameters for GammaShapeRate prior\ngamma_scale_factor[1,type=float]        # Parameters for GammaShapeRate prior\n\n# Visualization parameters\nx_limits[2,type=float]            # Plot boundaries (x-axis)\ny_limits[2,type=float]            # Plot boundaries (y-axis)\nfps[1,type=int]                   # Animation frames per second\nheatmap_resolution[1,type=int]    # Heatmap resolution\nplot_width[1,type=int]            # Plot width\nplot_height[1,type=int]           # Plot height\nagent_alpha[1,type=float]         # Visualization alpha for agents\ntarget_alpha[1,type=float]        # Visualization alpha for targets\ncolor_palette[1,type=string]      # Color palette for visualization\n\n# Environment definitions\ndoor_obstacle_center_1[2,type=float]    # Door environment, obstacle 1 center\ndoor_obstacle_size_1[2,type=float]      # Door environment, obstacle 1 size\ndoor_obstacle_center_2[2,type=float]    # Door environment, obstacle 2 center\ndoor_obstacle_size_2[2,type=float]      # Door environment, obstacle 2 size\n\nwall_obstacle_center[2,type=float]      # Wall environment, obstacle center\nwall_obstacle_size[2,type=float]        # Wall environment, obstacle size\n\ncombined_obstacle_center_1[2,type=float] # Combined environment, obstacle 1 center\ncombined_obstacle_size_1[2,type=float]   # Combined environment, obstacle 1 size\ncombined_obstacle_center_2[2,type=float] # Combined environment, obstacle 2 center\ncombined_obstacle_size_2[2,type=float]   # Combined environment, obstacle 2 size\ncombined_obstacle_center_3[2,type=float] # Combined environment, obstacle 3 center\ncombined_obstacle_size_3[2,type=float]   # Combined environment, obstacle 3 size\n\n# Agent configurations\nagent1_id[1,type=int]                   # Agent 1 ID\nagent1_radius[1,type=float]             # Agent 1 radius\nagent1_initial_position[2,type=float]   # Agent 1 initial position\nagent1_target_position[2,type=float]    # Agent 1 target position\n\nagent2_id[1,type=int]                   # Agent 2 ID\nagent2_radius[1,type=float]             # Agent 2 radius\nagent2_initial_position[2,type=float]   # Agent 2 initial position\nagent2_target_position[2,type=float]    # Agent 2 target position\n\nagent3_id[1,type=int]                   # Agent 3 ID\nagent3_radius[1,type=float]             # Agent 3 radius\nagent3_initial_position[2,type=float]   # Agent 3 initial position\nagent3_target_position[2,type=float]    # Agent 3 target position\n\nagent4_id[1,type=int]                   # Agent 4 ID\nagent4_radius[1,type=float]             # Agent 4 radius\nagent4_initial_position[2,type=float]   # Agent 4 initial position\nagent4_target_position[2,type=float]    # Agent 4 target position\n\n# Experiment configurations\nexperiment_seeds[2,type=int]            # Random seeds for reproducibility\nresults_dir[1,type=string]              # Base directory for results\nanimation_template[1,type=string]       # Filename template for animations\ncontrol_vis_filename[1,type=string]     # Filename for control visualization\nobstacle_distance_filename[1,type=string] # Filename for obstacle distance plot\npath_uncertainty_filename[1,type=string]  # Filename for path uncertainty plot\nconvergence_filename[1,type=string]       # Filename for convergence plot",
    "Connections": "# Model parameters\ndt > A\n(A, B, C) > state_space_model\n\n# Agent trajectories\n(state_space_model, initial_state_variance, control_variance) > agent_trajectories\n\n# Goal constraints\n(agent_trajectories, goal_constraint_variance) > goal_directed_behavior\n\n# Obstacle avoidance\n(agent_trajectories, gamma, gamma_shape, gamma_scale_factor) > obstacle_avoidance\n\n# Collision avoidance\n(agent_trajectories, nr_agents) > collision_avoidance\n\n# Complete planning system\n(goal_directed_behavior, obstacle_avoidance, collision_avoidance) > planning_system",
    "InitialParameterization": "# Model parameters\ndt=1.0\ngamma=1.0\nnr_steps=40\nnr_iterations=350\nnr_agents=4\nsoftmin_temperature=10.0\nintermediate_steps=10\nsave_intermediates=false\n\n# State space matrices\n# A = [1 dt 0 0; 0 1 0 0; 0 0 1 dt; 0 0 0 1]\nA={(1.0, 1.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.0, 0.0, 1.0)}\n\n# B = [0 0; dt 0; 0 0; 0 dt]\nB={(0.0, 0.0), (1.0, 0.0), (0.0, 0.0), (0.0, 1.0)}\n\n# C = [1 0 0 0; 0 0 1 0]\nC={(1.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)}\n\n# Prior distributions\ninitial_state_variance=100.0\ncontrol_variance=0.1\ngoal_constraint_variance=0.00001\ngamma_shape=1.5\ngamma_scale_factor=0.5\n\n# Visualization parameters\nx_limits={(-20, 20)}\ny_limits={(-20, 20)}\nfps=15\nheatmap_resolution=100\nplot_width=800\nplot_height=400\nagent_alpha=1.0\ntarget_alpha=0.2\ncolor_palette=\"tab10\"\n\n# Environment definitions\ndoor_obstacle_center_1={(-40.0, 0.0)}\ndoor_obstacle_size_1={(70.0, 5.0)}\ndoor_obstacle_center_2={(40.0, 0.0)}\ndoor_obstacle_size_2={(70.0, 5.0)}\n\nwall_obstacle_center={(0.0, 0.0)}\nwall_obstacle_size={(10.0, 5.0)}\n\ncombined_obstacle_center_1={(-50.0, 0.0)}\ncombined_obstacle_size_1={(70.0, 2.0)}\ncombined_obstacle_center_2={(50.0, 0.0)}\ncombined_obstacle_size_2={(70.0, 2.0)}\ncombined_obstacle_center_3={(5.0, -1.0)}\ncombined_obstacle_size_3={(3.0, 10.0)}\n\n# Agent configurations\nagent1_id=1\nagent1_radius=2.5\nagent1_initial_position={(-4.0, 10.0)}\nagent1_target_position={(-10.0, -10.0)}\n\nagent2_id=2\nagent2_radius=1.5\nagent2_initial_position={(-10.0, 5.0)}\nagent2_target_position={(10.0, -15.0)}\n\nagent3_id=3\nagent3_radius=1.0\nagent3_initial_position={(-15.0, -10.0)}\nagent3_target_position={(10.0, 10.0)}\n\nagent4_id=4\nagent4_radius=2.5\nagent4_initial_position={(0.0, -10.0)}\nagent4_target_position={(-10.0, 15.0)}\n\n# Experiment configurations\nexperiment_seeds={(42, 123)}\nresults_dir=\"results\"\nanimation_template=\"{environment}_{seed}.gif\"\ncontrol_vis_filename=\"control_signals.gif\"\nobstacle_distance_filename=\"obstacle_distance.png\"\npath_uncertainty_filename=\"path_uncertainty.png\"\nconvergence_filename=\"convergence.png\"",
    "InitialParameterization_raw_content": "# Model parameters\ndt=1.0\ngamma=1.0\nnr_steps=40\nnr_iterations=350\nnr_agents=4\nsoftmin_temperature=10.0\nintermediate_steps=10\nsave_intermediates=false\n\n# State space matrices\n# A = [1 dt 0 0; 0 1 0 0; 0 0 1 dt; 0 0 0 1]\nA={(1.0, 1.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.0, 0.0, 1.0)}\n\n# B = [0 0; dt 0; 0 0; 0 dt]\nB={(0.0, 0.0), (1.0, 0.0), (0.0, 0.0), (0.0, 1.0)}\n\n# C = [1 0 0 0; 0 0 1 0]\nC={(1.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)}\n\n# Prior distributions\ninitial_state_variance=100.0\ncontrol_variance=0.1\ngoal_constraint_variance=0.00001\ngamma_shape=1.5\ngamma_scale_factor=0.5\n\n# Visualization parameters\nx_limits={(-20, 20)}\ny_limits={(-20, 20)}\nfps=15\nheatmap_resolution=100\nplot_width=800\nplot_height=400\nagent_alpha=1.0\ntarget_alpha=0.2\ncolor_palette=\"tab10\"\n\n# Environment definitions\ndoor_obstacle_center_1={(-40.0, 0.0)}\ndoor_obstacle_size_1={(70.0, 5.0)}\ndoor_obstacle_center_2={(40.0, 0.0)}\ndoor_obstacle_size_2={(70.0, 5.0)}\n\nwall_obstacle_center={(0.0, 0.0)}\nwall_obstacle_size={(10.0, 5.0)}\n\ncombined_obstacle_center_1={(-50.0, 0.0)}\ncombined_obstacle_size_1={(70.0, 2.0)}\ncombined_obstacle_center_2={(50.0, 0.0)}\ncombined_obstacle_size_2={(70.0, 2.0)}\ncombined_obstacle_center_3={(5.0, -1.0)}\ncombined_obstacle_size_3={(3.0, 10.0)}\n\n# Agent configurations\nagent1_id=1\nagent1_radius=2.5\nagent1_initial_position={(-4.0, 10.0)}\nagent1_target_position={(-10.0, -10.0)}\n\nagent2_id=2\nagent2_radius=1.5\nagent2_initial_position={(-10.0, 5.0)}\nagent2_target_position={(10.0, -15.0)}\n\nagent3_id=3\nagent3_radius=1.0\nagent3_initial_position={(-15.0, -10.0)}\nagent3_target_position={(10.0, 10.0)}\n\nagent4_id=4\nagent4_radius=2.5\nagent4_initial_position={(0.0, -10.0)}\nagent4_target_position={(-10.0, 15.0)}\n\n# Experiment configurations\nexperiment_seeds={(42, 123)}\nresults_dir=\"results\"\nanimation_template=\"{environment}_{seed}.gif\"\ncontrol_vis_filename=\"control_signals.gif\"\nobstacle_distance_filename=\"obstacle_distance.png\"\npath_uncertainty_filename=\"path_uncertainty.png\"\nconvergence_filename=\"convergence.png\"",
    "Equations": "# State space model:\n# x_{t+1} = A * x_t + B * u_t + w_t,  w_t ~ N(0, control_variance)\n# y_t = C * x_t + v_t,                v_t ~ N(0, observation_variance)\n#\n# Obstacle avoidance constraint:\n# p(x_t | obstacle) ~ N(d(x_t, obstacle), gamma)\n# where d(x_t, obstacle) is the distance from position x_t to the nearest obstacle\n#\n# Goal constraint:\n# p(x_T | goal) ~ N(goal, goal_constraint_variance)\n# where x_T is the final position\n#\n# Collision avoidance constraint:\n# p(x_i, x_j) ~ N(||x_i - x_j|| - (r_i + r_j), gamma)\n# where x_i, x_j are positions of agents i and j, r_i, r_j are their radii",
    "Time": "Dynamic\nDiscreteTime\nModelTimeHorizon=nr_steps",
    "ActInfOntologyAnnotation": "dt=TimeStep\ngamma=ConstraintParameter\nnr_steps=TrajectoryLength\nnr_iterations=InferenceIterations\nnr_agents=NumberOfAgents\nsoftmin_temperature=SoftminTemperature\nA=StateTransitionMatrix\nB=ControlInputMatrix\nC=ObservationMatrix\ninitial_state_variance=InitialStateVariance\ncontrol_variance=ControlVariance\ngoal_constraint_variance=GoalConstraintVariance",
    "ModelParameters": "nr_agents=4\nnr_steps=40\nnr_iterations=350",
    "Footer": "Multi-agent Trajectory Planning - GNN Representation for RxInfer.jl",
    "Signature": "Creator: AI Assistant for GNN\nDate: 2024-07-27\nStatus: Example for RxInfer.jl multi-agent trajectory planning"
  },
  "other_sections": {},
  "gnnsection": {},
  "gnnversionandflags": {},
  "equations": "# State space model:\n# x_{t+1} = A * x_t + B * u_t + w_t,  w_t ~ N(0, control_variance)\n# y_t = C * x_t + v_t,                v_t ~ N(0, observation_variance)\n#\n# Obstacle avoidance constraint:\n# p(x_t | obstacle) ~ N(d(x_t, obstacle), gamma)\n# where d(x_t, obstacle) is the distance from position x_t to the nearest obstacle\n#\n# Goal constraint:\n# p(x_T | goal) ~ N(goal, goal_constraint_variance)\n# where x_T is the final position\n#\n# Collision avoidance constraint:\n# p(x_i, x_j) ~ N(||x_i - x_j|| - (r_i + r_j), gamma)\n# where x_i, x_j are positions of agents i and j, r_i, r_j are their radii",
  "ModelParameters": {},
  "footer": "Multi-agent Trajectory Planning - GNN Representation for RxInfer.jl"
}

rxinfer_multiagent_gnn.json

Text/Log Files

rxinfer_multiagent_gnn.txt

GNN Model Summary: Multi-agent Trajectory Planning
Source File: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples/rxinfer_multiagent_gnn.md

Metadata:
  description: This model represents a multi-agent trajectory planning scenario in RxInfer.jl.
It includes:
- State space model for agents moving in a 2D environment
- Obstacle avoidance constraints
- Goal-directed behavior
- Inter-agent collision avoidance
The model can be used to simulate trajectory planning in various environments with obstacles.

States (60):
  - ID: dt (dimensions=1,type=float, original_id=dt)
  - ID: gamma (dimensions=1,type=float, original_id=gamma)
  - ID: nr_steps (dimensions=1,type=int, original_id=nr_steps)
  - ID: nr_iterations (dimensions=1,type=int, original_id=nr_iterations)
  - ID: nr_agents (dimensions=1,type=int, original_id=nr_agents)
  - ID: softmin_temperature (dimensions=1,type=float, original_id=softmin_temperature)
  - ID: intermediate_steps (dimensions=1,type=int, original_id=intermediate_steps)
  - ID: save_intermediates (dimensions=1,type=bool, original_id=save_intermediates)
  - ID: A (dimensions=4,4,type=float, original_id=A)
  - ID: B (dimensions=4,2,type=float, original_id=B)
  - ID: C (dimensions=2,4,type=float, original_id=C)
  - ID: initial_state_variance (dimensions=1,type=float, original_id=initial_state_variance)
  - ID: control_variance (dimensions=1,type=float, original_id=control_variance)
  - ID: goal_constraint_variance (dimensions=1,type=float, original_id=goal_constraint_variance)
  - ID: gamma_shape (dimensions=1,type=float, original_id=gamma_shape)
  - ID: gamma_scale_factor (dimensions=1,type=float, original_id=gamma_scale_factor)
  - ID: x_limits (dimensions=2,type=float, original_id=x_limits)
  - ID: y_limits (dimensions=2,type=float, original_id=y_limits)
  - ID: fps (dimensions=1,type=int, original_id=fps)
  - ID: heatmap_resolution (dimensions=1,type=int, original_id=heatmap_resolution)
  - ID: plot_width (dimensions=1,type=int, original_id=plot_width)
  - ID: plot_height (dimensions=1,type=int, original_id=plot_height)
  - ID: agent_alpha (dimensions=1,type=float, original_id=agent_alpha)
  - ID: target_alpha (dimensions=1,type=float, original_id=target_alpha)
  - ID: color_palette (dimensions=1,type=string, original_id=color_palette)
  - ID: door_obstacle_center_1 (dimensions=2,type=float, original_id=door_obstacle_center_1)
  - ID: door_obstacle_size_1 (dimensions=2,type=float, original_id=door_obstacle_size_1)
  - ID: door_obstacle_center_2 (dimensions=2,type=float, original_id=door_obstacle_center_2)
  - ID: door_obstacle_size_2 (dimensions=2,type=float, original_id=door_obstacle_size_2)
  - ID: wall_obstacle_center (dimensions=2,type=float, original_id=wall_obstacle_center)
  - ID: wall_obstacle_size (dimensions=2,type=float, original_id=wall_obstacle_size)
  - ID: combined_obstacle_center_1 (dimensions=2,type=float, original_id=combined_obstacle_center_1)
  - ID: combined_obstacle_size_1 (dimensions=2,type=float, original_id=combined_obstacle_size_1)
  - ID: combined_obstacle_center_2 (dimensions=2,type=float, original_id=combined_obstacle_center_2)
  - ID: combined_obstacle_size_2 (dimensions=2,type=float, original_id=combined_obstacle_size_2)
  - ID: combined_obstacle_center_3 (dimensions=2,type=float, original_id=combined_obstacle_center_3)
  - ID: combined_obstacle_size_3 (dimensions=2,type=float, original_id=combined_obstacle_size_3)
  - ID: agent1_id (dimensions=1,type=int, original_id=agent1_id)
  - ID: agent1_radius (dimensions=1,type=float, original_id=agent1_radius)
  - ID: agent1_initial_position (dimensions=2,type=float, original_id=agent1_initial_position)
  - ID: agent1_target_position (dimensions=2,type=float, original_id=agent1_target_position)
  - ID: agent2_id (dimensions=1,type=int, original_id=agent2_id)
  - ID: agent2_radius (dimensions=1,type=float, original_id=agent2_radius)
  - ID: agent2_initial_position (dimensions=2,type=float, original_id=agent2_initial_position)
  - ID: agent2_target_position (dimensions=2,type=float, original_id=agent2_target_position)
  - ID: agent3_id (dimensions=1,type=int, original_id=agent3_id)
  - ID: agent3_radius (dimensions=1,type=float, original_id=agent3_radius)
  - ID: agent3_initial_position (dimensions=2,type=float, original_id=agent3_initial_position)
  - ID: agent3_target_position (dimensions=2,type=float, original_id=agent3_target_position)
  - ID: agent4_id (dimensions=1,type=int, original_id=agent4_id)
  - ID: agent4_radius (dimensions=1,type=float, original_id=agent4_radius)
  - ID: agent4_initial_position (dimensions=2,type=float, original_id=agent4_initial_position)
  - ID: agent4_target_position (dimensions=2,type=float, original_id=agent4_target_position)
  - ID: experiment_seeds (dimensions=2,type=int, original_id=experiment_seeds)
  - ID: results_dir (dimensions=1,type=string, original_id=results_dir)
  - ID: animation_template (dimensions=1,type=string, original_id=animation_template)
  - ID: control_vis_filename (dimensions=1,type=string, original_id=control_vis_filename)
  - ID: obstacle_distance_filename (dimensions=1,type=string, original_id=obstacle_distance_filename)
  - ID: path_uncertainty_filename (dimensions=1,type=string, original_id=path_uncertainty_filename)
  - ID: convergence_filename (dimensions=1,type=string, original_id=convergence_filename)

Initial Parameters (0):

General Parameters (0):

Observations (0):

Transitions (7):
  - None -> None
  - None -> None
  - None -> None
  - None -> None
  - None -> None
  - None -> None
  - None -> None

Ontology Annotations (12):
  dt = TimeStep
  gamma = ConstraintParameter
  nr_steps = TrajectoryLength
  nr_iterations = InferenceIterations
  nr_agents = NumberOfAgents
  softmin_temperature = SoftminTemperature
  A = StateTransitionMatrix
  B = ControlInputMatrix
  C = ObservationMatrix
  initial_state_variance = InitialStateVariance

... (file truncated, total lines: 103)

rxinfer_multiagent_gnn.txt

Other Files

GNN Processing Summary (Overall File List)

📊 GNN Processing Summary

🗓️ Generated: 2025-06-06 13:41:27

⚙️ Processing Configuration

Configuration details not available

📁 GNN Files Discovered

Found 2 GNN files for processing:

src/gnn/examples/pymdp_pomdp_agent.md
src/gnn/examples/rxinfer_multiagent_gnn.md

🔄 Pipeline Execution Status

Pipeline execution data not available.

📊 Output Summary

GNN Processing: 1 files in gnn_processing_step/
Type Checking: 11 files in gnn_type_check/
Exports: 9 files in gnn_exports/
Visualizations: 0 files in gnn_examples_visualization/
Rendered Simulators: 0 files in gnn_rendered_simulators/
Test Reports: 1 files in test_reports/

🔍 Key Findings

Test Results: 52/80 tests passed (28 failures, 0 errors)

📋 Recommendations

General Improvements

Review and fix any test failures in the PyMDP converter components
Investigate GNN section parsing issues (StateSpaceBlock/Connections detection)
Consider running pipeline with --verbose flag for detailed debugging
Check individual step logs in the output directory for specific error details

Report generated by GNN Processing Pipeline Step 5 (Export)

GNN Visualizations (Step 6)

Visualizations for pymdp_pomdp_agent: pymdp_pomdp_agent

Images

combined_matrices.png

combined_visualization.png

connections.png

matrix_Am0.png

matrix_Am1.png

matrix_Am2.png

matrix_Bf0.png

matrix_Bf1.png

matrix_Cm0.png

matrix_Cm1.png

matrix_Cm2.png

matrix_Df0.png

matrix_Df1.png

ontology_annotations.png

state_space.png

Markdown Reports

file_content.md

GNN File: src/gnn/examples/pymdp_pomdp_agent.md\n\n## Raw File Content\n\n```\n# GNN Example: Multifactor PyMDP Agent

Format: Markdown representation of a Multifactor PyMDP model in Active Inference format

Version: 1.0

This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.

GNNSection

MultifactorPyMDPAgent

GNNVersionAndFlags

GNN v1

ModelName

Multifactor PyMDP Agent v1

ModelAnnotation

This model represents a PyMDP agent with multiple observation modalities and hidden state factors. - Observation modalities: "state_observation" (3 outcomes), "reward" (3 outcomes), "decision_proprioceptive" (3 outcomes) - Hidden state factors: "reward_level" (2 states), "decision_state" (3 states) - Control: "decision_state" factor is controllable with 3 possible actions. The parameterization is derived from a PyMDP Python script example.

StateSpaceBlock

A_matrices are defined per modality: A_m[observation_outcomes, state_factor0_states, state_factor1_states]

A_m0[3,2,3,type=float] # Likelihood for modality 0 ("state_observation") A_m1[3,2,3,type=float] # Likelihood for modality 1 ("reward") A_m2[3,2,3,type=float] # Likelihood for modality 2 ("decision_proprioceptive")

B_matrices are defined per hidden state factor: B_f[states_next, states_previous, actions]

B_f0[2,2,1,type=float] # Transitions for factor 0 ("reward_level"), 1 implicit action (uncontrolled) B_f1[3,3,3,type=float] # Transitions for factor 1 ("decision_state"), 3 actions

C_vectors are defined per modality: C_m[observation_outcomes]

C_m0[3,type=float] # Preferences for modality 0 C_m1[3,type=float] # Preferences for modality 1 C_m2[3,type=float] # Preferences for modality 2

D_vectors are defined per hidden state factor: D_f[states]

D_f0[2,type=float] # Prior for factor 0 D_f1[3,type=float] # Prior for factor 1

Hidden States

s_f0[2,1,type=float] # Hidden state for factor 0 ("reward_level") s_f1[3,1,type=float] # Hidden state for factor 1 ("decision_state") s_prime_f0[2,1,type=float] # Next hidden state for factor 0 s_prime_f1[3,1,type=float] # Next hidden state for factor 1

Observations

o_m0[3,1,type=float] # Observation for modality 0 o_m1[3,1,type=float] # Observation for modality 1 o_m2[3,1,type=float] # Observation for modality 2

Policy and Control

π_f1[3,type=float] # Policy (distribution over actions) for controllable factor 1 u_f1[1,type=int] # Action taken for controllable factor 1 G[1,type=float] # Expected Free Energy (overall, or can be per policy) t[1,type=int] # Time step

Connections

(D_f0,D_f1)-(s_f0,s_f1) (s_f0,s_f1)-(A_m0,A_m1,A_m2) (A_m0,A_m1,A_m2)-(o_m0,o_m1,o_m2) (s_f0,s_f1,u_f1)-(B_f0,B_f1) # u_f1 primarily affects B_f1; B_f0 is uncontrolled (B_f0,B_f1)-(s_prime_f0,s_prime_f1) (C_m0,C_m1,C_m2)>G G>π_f1 π_f1-u_f1 G=ExpectedFreeEnergy t=Time

InitialParameterization

A_m0: num_obs[0]=3, num_states[0]=2, num_states[1]=3. Format: A[obs_idx][state_f0_idx][state_f1_idx]

A[0][:, :, 0] = np.ones((3,2))/3

A[0][:, :, 1] = np.ones((3,2))/3

A[0][:, :, 2] = [[0.8,0.2],[0.0,0.0],[0.2,0.8]] (obs x state_f0 for state_f1=2)

A_m0={ ( (0.33333,0.33333,0.8), (0.33333,0.33333,0.2) ), # obs=0; (vals for s_f1 over s_f0=0), (vals for s_f1 over s_f0=1) ( (0.33333,0.33333,0.0), (0.33333,0.33333,0.0) ), # obs=1 ( (0.33333,0.33333,0.2), (0.33333,0.33333,0.8) ) # obs=2 }

A_m1: num_obs[1]=3, num_states[0]=2, num_states[1]=3

A[1][2, :, 0] = [1.0,1.0]

A[1][0:2, :, 1] = softmax([[1,0],[0,1]]) approx [[0.731,0.269],[0.269,0.731]]

A[1][2, :, 2] = [1.0,1.0]

Others are 0.

A_m1={ ( (0.0,0.731,0.0), (0.0,0.269,0.0) ), # obs=0 ( (0.0,0.269,0.0), (0.0,0.731,0.0) ), # obs=1 ( (1.0,0.0,1.0), (1.0,0.0,1.0) ) # obs=2 }

A_m2: num_obs[2]=3, num_states[0]=2, num_states[1]=3

A[2][0,:,0]=1.0; A[2][1,:,1]=1.0; A[2][2,:,2]=1.0

Others are 0.

A_m2={ ( (1.0,0.0,0.0), (1.0,0.0,0.0) ), # obs=0 ( (0.0,1.0,0.0), (0.0,1.0,0.0) ), # obs=1 ( (0.0,0.0,1.0), (0.0,0.0,1.0) ) # obs=2 }

B_f0: factor 0 (2 states), uncontrolled (1 action). Format B[s_next, s_prev, action=0]

B_f0 = eye(2)

B_f0={ ( (1.0),(0.0) ), # s_next=0; (vals for s_prev over action=0) ( (0.0),(1.0) ) # s_next=1 }

B_f1: factor 1 (3 states), 3 actions. Format B[s_next, s_prev, action_idx]

B_f1[:,:,action_idx] = eye(3) for each action

B_f1={ ( (1.0,1.0,1.0), (0.0,0.0,0.0), (0.0,0.0,0.0) ), # s_next=0; (vals for actions over s_prev=0), (vals for actions over s_prev=1), ... ( (0.0,0.0,0.0), (1.0,1.0,1.0), (0.0,0.0,0.0) ), # s_next=1 ( (0.0,0.0,0.0), (0.0,0.0,0.0), (1.0,1.0,1.0) ) # s_next=2 }

C_m0: num_obs[0]=3. Defaults to zeros.

C_m0={(0.0,0.0,0.0)}

C_m1: num_obs[1]=3. C[1][0]=1.0, C[1][1]=-2.0

C_m1={(1.0,-2.0,0.0)}

C_m2: num_obs[2]=3. Defaults to zeros.

C_m2={(0.0,0.0,0.0)}

D_f0: factor 0 (2 states). Uniform prior.

D_f0={(0.5,0.5)}

D_f1: factor 1 (3 states). Uniform prior.

D_f1={(0.33333,0.33333,0.33333)}

Equations

Standard PyMDP agent equations for state inference (infer_states),

policy inference (infer_policies), and action sampling (sample_action).

qs = infer_states(o)

q_pi, efe = infer_policies()

action = sample_action()

Time

Dynamic DiscreteTime=t ModelTimeHorizon=Unbounded # Agent definition is generally unbounded, specific simulation runs have a horizon.

ActInfOntologyAnnotation

A_m0=LikelihoodMatrixModality0 A_m1=LikelihoodMatrixModality1 A_m2=LikelihoodMatrixModality2 B_f0=TransitionMatrixFactor0 B_f1=TransitionMatrixFactor1 C_m0=LogPreferenceVectorModality0 C_m1=LogPreferenceVectorModality1 C_m2=LogPreferenceVectorModality2 D_f0=PriorOverHiddenStatesFactor0 D_f1=PriorOverHiddenStatesFactor1 s_f0=HiddenStateFactor0 s_f1=HiddenStateFactor1 s_prime_f0=NextHiddenStateFactor0 s_prime_f1=NextHiddenStateFactor1 o_m0=ObservationModality0 o_m1=ObservationModality1 o_m2=ObservationModality2 π_f1=PolicyVectorFactor1 # Distribution over actions for factor 1 u_f1=ActionFactor1 # Chosen action for factor 1 G=ExpectedFreeEnergy

ModelParameters

num_hidden_states_factors: [2, 3] # s_f0[2], s_f1[3] num_obs_modalities: [3, 3, 3] # o_m0[3], o_m1[3], o_m2[3] num_control_factors: [1, 3] # B_f0 actions_dim=1 (uncontrolled), B_f1 actions_dim=3 (controlled by pi_f1)

Footer

Multifactor PyMDP Agent v1 - GNN Representation

Signature

NA \n```\n\n## Parsed Sections

_HeaderComments

# GNN Example: Multifactor PyMDP Agent
# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format
# Version: 1.0
# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.

ModelName

Multifactor PyMDP Agent v1

GNNSection

MultifactorPyMDPAgent

GNNVersionAndFlags

GNN v1

ModelAnnotation

This model represents a PyMDP agent with multiple observation modalities and hidden state factors.
- Observation modalities: "state_observation" (3 outcomes), "reward" (3 outcomes), "decision_proprioceptive" (3 outcomes)
- Hidden state factors: "reward_level" (2 states), "decision_state" (3 states)
- Control: "decision_state" factor is controllable with 3 possible actions.
The parameterization is derived from a PyMDP Python script example.

StateSpaceBlock

# A_matrices are defined per modality: A_m[observation_outcomes, state_factor0_states, state_factor1_states]
A_m0[3,2,3,type=float]   # Likelihood for modality 0 ("state_observation")
A_m1[3,2,3,type=float]   # Likelihood for modality 1 ("reward")
A_m2[3,2,3,type=float]   # Likelihood for modality 2 ("decision_proprioceptive")

# B_matrices are defined per hidden state factor: B_f[states_next, states_previous, actions]
B_f0[2,2,1,type=float]   # Transitions for factor 0 ("reward_level"), 1 implicit action (uncontrolled)
B_f1[3,3,3,type=float]   # Transitions for factor 1 ("decision_state"), 3 actions

# C_vectors are defined per modality: C_m[observation_outcomes]
C_m0[3,type=float]       # Preferences for modality 0
C_m1[3,type=float]       # Preferences for modality 1
C_m2[3,type=float]       # Preferences for modality 2

# D_vectors are defined per hidden state factor: D_f[states]
D_f0[2,type=float]       # Prior for factor 0
D_f1[3,type=float]       # Prior for factor 1

# Hidden States
s_f0[2,1,type=float]     # Hidden state for factor 0 ("reward_level")
s_f1[3,1,type=float]     # Hidden state for factor 1 ("decision_state")
s_prime_f0[2,1,type=float] # Next hidden state for factor 0
s_prime_f1[3,1,type=float] # Next hidden state for factor 1

# Observations
o_m0[3,1,type=float]     # Observation for modality 0
o_m1[3,1,type=float]     # Observation for modality 1
o_m2[3,1,type=float]     # Observation for modality 2

# Policy and Control
π_f1[3,type=float]       # Policy (distribution over actions) for controllable factor 1
u_f1[1,type=int]         # Action taken for controllable factor 1
G[1,type=float]          # Expected Free Energy (overall, or can be per policy)
t[1,type=int]            # Time step

Connections

(D_f0,D_f1)-(s_f0,s_f1)
(s_f0,s_f1)-(A_m0,A_m1,A_m2)
(A_m0,A_m1,A_m2)-(o_m0,o_m1,o_m2)
(s_f0,s_f1,u_f1)-(B_f0,B_f1) # u_f1 primarily affects B_f1; B_f0 is uncontrolled
(B_f0,B_f1)-(s_prime_f0,s_prime_f1)
(C_m0,C_m1,C_m2)>G
G>π_f1
π_f1-u_f1
G=ExpectedFreeEnergy
t=Time

InitialParameterization

# A_m0: num_obs[0]=3, num_states[0]=2, num_states[1]=3. Format: A[obs_idx][state_f0_idx][state_f1_idx]
# A[0][:, :, 0] = np.ones((3,2))/3
# A[0][:, :, 1] = np.ones((3,2))/3
# A[0][:, :, 2] = [[0.8,0.2],[0.0,0.0],[0.2,0.8]] (obs x state_f0 for state_f1=2)
A_m0={
  ( (0.33333,0.33333,0.8), (0.33333,0.33333,0.2) ),  # obs=0; (vals for s_f1 over s_f0=0), (vals for s_f1 over s_f0=1)
  ( (0.33333,0.33333,0.0), (0.33333,0.33333,0.0) ),  # obs=1
  ( (0.33333,0.33333,0.2), (0.33333,0.33333,0.8) )   # obs=2
}

# A_m1: num_obs[1]=3, num_states[0]=2, num_states[1]=3
# A[1][2, :, 0] = [1.0,1.0]
# A[1][0:2, :, 1] = softmax([[1,0],[0,1]]) approx [[0.731,0.269],[0.269,0.731]]
# A[1][2, :, 2] = [1.0,1.0]
# Others are 0.
A_m1={
  ( (0.0,0.731,0.0), (0.0,0.269,0.0) ),  # obs=0
  ( (0.0,0.269,0.0), (0.0,0.731,0.0) ),  # obs=1
  ( (1.0,0.0,1.0), (1.0,0.0,1.0) )      # obs=2
}

# A_m2: num_obs[2]=3, num_states[0]=2, num_states[1]=3
# A[2][0,:,0]=1.0; A[2][1,:,1]=1.0; A[2][2,:,2]=1.0
# Others are 0.
A_m2={
  ( (1.0,0.0,0.0), (1.0,0.0,0.0) ),  # obs=0
  ( (0.0,1.0,0.0), (0.0,1.0,0.0) ),  # obs=1
  ( (0.0,0.0,1.0), (0.0,0.0,1.0) )   # obs=2
}

# B_f0: factor 0 (2 states), uncontrolled (1 action). Format B[s_next, s_prev, action=0]
# B_f0 = eye(2)
B_f0={
  ( (1.0),(0.0) ), # s_next=0; (vals for s_prev over action=0)
  ( (0.0),(1.0) )  # s_next=1
}

# B_f1: factor 1 (3 states), 3 actions. Format B[s_next, s_prev, action_idx]
# B_f1[:,:,action_idx] = eye(3) for each action
B_f1={
  ( (1.0,1.0,1.0), (0.0,0.0,0.0), (0.0,0.0,0.0) ), # s_next=0; (vals for actions over s_prev=0), (vals for actions over s_prev=1), ...
  ( (0.0,0.0,0.0), (1.0,1.0,1.0), (0.0,0.0,0.0) ), # s_next=1
  ( (0.0,0.0,0.0), (0.0,0.0,0.0), (1.0,1.0,1.0) )  # s_next=2
}

# C_m0: num_obs[0]=3. Defaults to zeros.
C_m0={(0.0,0.0,0.0)}

# C_m1: num_obs[1]=3. C[1][0]=1.0, C[1][1]=-2.0
C_m1={(1.0,-2.0,0.0)}

# C_m2: num_obs[2]=3. Defaults to zeros.
C_m2={(0.0,0.0,0.0)}

# D_f0: factor 0 (2 states). Uniform prior.
D_f0={(0.5,0.5)}

# D_f1: factor 1 (3 states). Uniform prior.
D_f1={(0.33333,0.33333,0.33333)}

Equations

# Standard PyMDP agent equations for state inference (infer_states),
# policy inference (infer_policies), and action sampling (sample_action).
# qs = infer_states(o)
# q_pi, efe = infer_policies()
# action = sample_action()

Time

Dynamic
DiscreteTime=t
ModelTimeHorizon=Unbounded # Agent definition is generally unbounded, specific simulation runs have a horizon.

ActInfOntologyAnnotation

A_m0=LikelihoodMatrixModality0
A_m1=LikelihoodMatrixModality1
A_m2=LikelihoodMatrixModality2
B_f0=TransitionMatrixFactor0
B_f1=TransitionMatrixFactor1
C_m0=LogPreferenceVectorModality0
C_m1=LogPreferenceVectorModality1
C_m2=LogPreferenceVectorModality2
D_f0=PriorOverHiddenStatesFactor0
D_f1=PriorOverHiddenStatesFactor1
s_f0=HiddenStateFactor0
s_f1=HiddenStateFactor1
s_prime_f0=NextHiddenStateFactor0
s_prime_f1=NextHiddenStateFactor1
o_m0=ObservationModality0
o_m1=ObservationModality1
o_m2=ObservationModality2
π_f1=PolicyVectorFactor1 # Distribution over actions for factor 1
u_f1=ActionFactor1       # Chosen action for factor 1
G=ExpectedFreeEnergy

ModelParameters

num_hidden_states_factors: [2, 3]  # s_f0[2], s_f1[3]
num_obs_modalities: [3, 3, 3]     # o_m0[3], o_m1[3], o_m2[3]
num_control_factors: [1, 3]   # B_f0 actions_dim=1 (uncontrolled), B_f1 actions_dim=3 (controlled by pi_f1)

Footer

Multifactor PyMDP Agent v1 - GNN Representation

Signature

NA

JSON Files

full_model_data.json

{
  "_HeaderComments": "# GNN Example: Multifactor PyMDP Agent\n# Format: Markdown representation of a Multifactor PyMDP model in Active Inference format\n# Version: 1.0\n# This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.",
  "ModelName": "Multifactor PyMDP Agent v1",
  "GNNSection": "MultifactorPyMDPAgent",
  "GNNVersionAndFlags": "GNN v1",
  "ModelAnnotation": "This model represents a PyMDP agent with multiple observation modalities and hidden state factors.\n- Observation modalities: \"state_observation\" (3 outcomes), \"reward\" (3 outcomes), \"decision_proprioceptive\" (3 outcomes)\n- Hidden state factors: \"reward_level\" (2 states), \"decision_state\" (3 states)\n- Control: \"decision_state\" factor is controllable with 3 possible actions.\nThe parameterization is derived from a PyMDP Python script example.",
  "StateSpaceBlock": "# A_matrices are defined per modality: A_m[observation_outcomes, state_factor0_states, state_factor1_states]\nA_m0[3,2,3,type=float]   # Likelihood for modality 0 (\"state_observation\")\nA_m1[3,2,3,type=float]   # Likelihood for modality 1 (\"reward\")\nA_m2[3,2,3,type=float]   # Likelihood for modality 2 (\"decision_proprioceptive\")\n\n# B_matrices are defined per hidden state factor: B_f[states_next, states_previous, actions]\nB_f0[2,2,1,type=float]   # Transitions for factor 0 (\"reward_level\"), 1 implicit action (uncontrolled)\nB_f1[3,3,3,type=float]   # Transitions for factor 1 (\"decision_state\"), 3 actions\n\n# C_vectors are defined per modality: C_m[observation_outcomes]\nC_m0[3,type=float]       # Preferences for modality 0\nC_m1[3,type=float]       # Preferences for modality 1\nC_m2[3,type=float]       # Preferences for modality 2\n\n# D_vectors are defined per hidden state factor: D_f[states]\nD_f0[2,type=float]       # Prior for factor 0\nD_f1[3,type=float]       # Prior for factor 1\n\n# Hidden States\ns_f0[2,1,type=float]     # Hidden state for factor 0 (\"reward_level\")\ns_f1[3,1,type=float]     # Hidden state for factor 1 (\"decision_state\")\ns_prime_f0[2,1,type=float] # Next hidden state for factor 0\ns_prime_f1[3,1,type=float] # Next hidden state for factor 1\n\n# Observations\no_m0[3,1,type=float]     # Observation for modality 0\no_m1[3,1,type=float]     # Observation for modality 1\no_m2[3,1,type=float]     # Observation for modality 2\n\n# Policy and Control\n\u03c0_f1[3,type=float]       # Policy (distribution over actions) for controllable factor 1\nu_f1[1,type=int]         # Action taken for controllable factor 1\nG[1,type=float]          # Expected Free Energy (overall, or can be per policy)\nt[1,type=int]            # Time step",
  "Connections": "(D_f0,D_f1)-(s_f0,s_f1)\n(s_f0,s_f1)-(A_m0,A_m1,A_m2)\n(A_m0,A_m1,A_m2)-(o_m0,o_m1,o_m2)\n(s_f0,s_f1,u_f1)-(B_f0,B_f1) # u_f1 primarily affects B_f1; B_f0 is uncontrolled\n(B_f0,B_f1)-(s_prime_f0,s_prime_f1)\n(C_m0,C_m1,C_m2)>G\nG>\u03c0_f1\n\u03c0_f1-u_f1\nG=ExpectedFreeEnergy\nt=Time",
  "InitialParameterization": "# A_m0: num_obs[0]=3, num_states[0]=2, num_states[1]=3. Format: A[obs_idx][state_f0_idx][state_f1_idx]\n# A[0][:, :, 0] = np.ones((3,2))/3\n# A[0][:, :, 1] = np.ones((3,2))/3\n# A[0][:, :, 2] = [[0.8,0.2],[0.0,0.0],[0.2,0.8]] (obs x state_f0 for state_f1=2)\nA_m0={\n  ( (0.33333,0.33333,0.8), (0.33333,0.33333,0.2) ),  # obs=0; (vals for s_f1 over s_f0=0), (vals for s_f1 over s_f0=1)\n  ( (0.33333,0.33333,0.0), (0.33333,0.33333,0.0) ),  # obs=1\n  ( (0.33333,0.33333,0.2), (0.33333,0.33333,0.8) )   # obs=2\n}\n\n# A_m1: num_obs[1]=3, num_states[0]=2, num_states[1]=3\n# A[1][2, :, 0] = [1.0,1.0]\n# A[1][0:2, :, 1] = softmax([[1,0],[0,1]]) approx [[0.731,0.269],[0.269,0.731]]\n# A[1][2, :, 2] = [1.0,1.0]\n# Others are 0.\nA_m1={\n  ( (0.0,0.731,0.0), (0.0,0.269,0.0) ),  # obs=0\n  ( (0.0,0.269,0.0), (0.0,0.731,0.0) ),  # obs=1\n  ( (1.0,0.0,1.0), (1.0,0.0,1.0) )      # obs=2\n}\n\n# A_m2: num_obs[2]=3, num_states[0]=2, num_states[1]=3\n# A[2][0,:,0]=1.0; A[2][1,:,1]=1.0; A[2][2,:,2]=1.0\n# Others are 0.\nA_m2={\n  ( (1.0,0.0,0.0), (1.0,0.0,0.0) ),  # obs=0\n  ( (0.0,1.0,0.0), (0.0,1.0,0.0) ),  # obs=1\n  ( (0.0,0.0,1.0), (0.0,0.0,1.0) )   # obs=2\n}\n\n# B_f0: factor 0 (2 states), uncontrolled (1 action). Format B[s_next, s_prev, action=0]\n# B_f0 = eye(2)\nB_f0={\n  ( (1.0),(0.0) ), # s_next=0; (vals for s_prev over action=0)\n  ( (0.0),(1.0) )  # s_next=1\n}\n\n# B_f1: factor 1 (3 states), 3 actions. Format B[s_next, s_prev, action_idx]\n# B_f1[:,:,action_idx] = eye(3) for each action\nB_f1={\n  ( (1.0,1.0,1.0), (0.0,0.0,0.0), (0.0,0.0,0.0) ), # s_next=0; (vals for actions over s_prev=0), (vals for actions over s_prev=1), ...\n  ( (0.0,0.0,0.0), (1.0,1.0,1.0), (0.0,0.0,0.0) ), # s_next=1\n  ( (0.0,0.0,0.0), (0.0,0.0,0.0), (1.0,1.0,1.0) )  # s_next=2\n}\n\n# C_m0: num_obs[0]=3. Defaults to zeros.\nC_m0={(0.0,0.0,0.0)}\n\n# C_m1: num_obs[1]=3. C[1][0]=1.0, C[1][1]=-2.0\nC_m1={(1.0,-2.0,0.0)}\n\n# C_m2: num_obs[2]=3. Defaults to zeros.\nC_m2={(0.0,0.0,0.0)}\n\n# D_f0: factor 0 (2 states). Uniform prior.\nD_f0={(0.5,0.5)}\n\n# D_f1: factor 1 (3 states). Uniform prior.\nD_f1={(0.33333,0.33333,0.33333)}",
  "Equations": "# Standard PyMDP agent equations for state inference (infer_states),\n# policy inference (infer_policies), and action sampling (sample_action).\n# qs = infer_states(o)\n# q_pi, efe = infer_policies()\n# action = sample_action()",
  "Time": "Dynamic\nDiscreteTime=t\nModelTimeHorizon=Unbounded # Agent definition is generally unbounded, specific simulation runs have a horizon.",
  "ActInfOntologyAnnotation": "A_m0=LikelihoodMatrixModality0\nA_m1=LikelihoodMatrixModality1\nA_m2=LikelihoodMatrixModality2\nB_f0=TransitionMatrixFactor0\nB_f1=TransitionMatrixFactor1\nC_m0=LogPreferenceVectorModality0\nC_m1=LogPreferenceVectorModality1\nC_m2=LogPreferenceVectorModality2\nD_f0=PriorOverHiddenStatesFactor0\nD_f1=PriorOverHiddenStatesFactor1\ns_f0=HiddenStateFactor0\ns_f1=HiddenStateFactor1\ns_prime_f0=NextHiddenStateFactor0\ns_prime_f1=NextHiddenStateFactor1\no_m0=ObservationModality0\no_m1=ObservationModality1\no_m2=ObservationModality2\n\u03c0_f1=PolicyVectorFactor1 # Distribution over actions for factor 1\nu_f1=ActionFactor1       # Chosen action for factor 1\nG=ExpectedFreeEnergy",
  "ModelParameters": "num_hidden_states_factors: [2, 3]  # s_f0[2], s_f1[3]\nnum_obs_modalities: [3, 3, 3]     # o_m0[3], o_m1[3], o_m2[3]\nnum_control_factors: [1, 3]   # B_f0 actions_dim=1 (uncontrolled), B_f1 actions_dim=3 (controlled by pi_f1)",
  "Footer": "Multifactor PyMDP Agent v1 - GNN Representation",
  "Signature": "NA"
}

full_model_data.json

model_metadata.json

{
  "ModelName": "Multifactor PyMDP Agent v1",
  "ModelAnnotation": "This model represents a PyMDP agent with multiple observation modalities and hidden state factors.\n- Observation modalities: \"state_observation\" (3 outcomes), \"reward\" (3 outcomes), \"decision_proprioceptive\" (3 outcomes)\n- Hidden state factors: \"reward_level\" (2 states), \"decision_state\" (3 states)\n- Control: \"decision_state\" factor is controllable with 3 possible actions.\nThe parameterization is derived from a PyMDP Python script example.",
  "GNNVersionAndFlags": "GNN v1",
  "Time": "Dynamic\nDiscreteTime=t\nModelTimeHorizon=Unbounded # Agent definition is generally unbounded, specific simulation runs have a horizon.",
  "ActInfOntologyAnnotation": "A_m0=LikelihoodMatrixModality0\nA_m1=LikelihoodMatrixModality1\nA_m2=LikelihoodMatrixModality2\nB_f0=TransitionMatrixFactor0\nB_f1=TransitionMatrixFactor1\nC_m0=LogPreferenceVectorModality0\nC_m1=LogPreferenceVectorModality1\nC_m2=LogPreferenceVectorModality2\nD_f0=PriorOverHiddenStatesFactor0\nD_f1=PriorOverHiddenStatesFactor1\ns_f0=HiddenStateFactor0\ns_f1=HiddenStateFactor1\ns_prime_f0=NextHiddenStateFactor0\ns_prime_f1=NextHiddenStateFactor1\no_m0=ObservationModality0\no_m1=ObservationModality1\no_m2=ObservationModality2\n\u03c0_f1=PolicyVectorFactor1 # Distribution over actions for factor 1\nu_f1=ActionFactor1       # Chosen action for factor 1\nG=ExpectedFreeEnergy"
}

model_metadata.json

Visualizations for rxinfer_multiagent_gnn: rxinfer_multiagent_gnn

Images

combined_matrices.png

combined_visualization.png

connections.png

matrix_A.png

matrix_B.png

matrix_C.png

matrix_agent1initialposition.png

matrix_agent1targetposition.png

matrix_agent2initialposition.png

matrix_agent2targetposition.png

matrix_agent3initialposition.png

matrix_agent3targetposition.png

matrix_agent4initialposition.png

matrix_agent4targetposition.png

matrix_combinedobstaclecenter1.png

matrix_combinedobstaclecenter2.png

matrix_combinedobstaclecenter3.png

matrix_combinedobstaclesize1.png

matrix_combinedobstaclesize2.png

matrix_combinedobstaclesize3.png

matrix_doorobstaclecenter1.png

matrix_doorobstaclecenter2.png

matrix_doorobstaclesize1.png

matrix_doorobstaclesize2.png

matrix_experimentseeds.png

matrix_wallobstaclecenter.png

matrix_wallobstaclesize.png

matrix_xlimits.png

matrix_ylimits.png

ontology_annotations.png

state_space.png

Markdown Reports

file_content.md

GNN File: src/gnn/examples/rxinfer_multiagent_gnn.md\n\n## Raw File Content\n\n```\n# GNN Example: RxInfer Multi-agent Trajectory Planning

Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl

Version: 1.0

This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.

GNNSection

RxInferMultiAgentTrajectoryPlanning

GNNVersionAndFlags

GNN v1

ModelName

Multi-agent Trajectory Planning

ModelAnnotation

This model represents a multi-agent trajectory planning scenario in RxInfer.jl. It includes: - State space model for agents moving in a 2D environment - Obstacle avoidance constraints - Goal-directed behavior - Inter-agent collision avoidance The model can be used to simulate trajectory planning in various environments with obstacles.

StateSpaceBlock

Model parameters

dt[1,type=float] # Time step for the state space model gamma[1,type=float] # Constraint parameter for the Halfspace node nr_steps[1,type=int] # Number of time steps in the trajectory nr_iterations[1,type=int] # Number of inference iterations nr_agents[1,type=int] # Number of agents in the simulation softmin_temperature[1,type=float] # Temperature parameter for the softmin function intermediate_steps[1,type=int] # Intermediate results saving interval save_intermediates[1,type=bool] # Whether to save intermediate results

State space matrices

A[4,4,type=float] # State transition matrix B[4,2,type=float] # Control input matrix C[2,4,type=float] # Observation matrix

Prior distributions

initial_state_variance[1,type=float] # Prior on initial state control_variance[1,type=float] # Prior on control inputs goal_constraint_variance[1,type=float] # Goal constraints variance gamma_shape[1,type=float] # Parameters for GammaShapeRate prior gamma_scale_factor[1,type=float] # Parameters for GammaShapeRate prior

Visualization parameters

x_limits[2,type=float] # Plot boundaries (x-axis) y_limits[2,type=float] # Plot boundaries (y-axis) fps[1,type=int] # Animation frames per second heatmap_resolution[1,type=int] # Heatmap resolution plot_width[1,type=int] # Plot width plot_height[1,type=int] # Plot height agent_alpha[1,type=float] # Visualization alpha for agents target_alpha[1,type=float] # Visualization alpha for targets color_palette[1,type=string] # Color palette for visualization

Environment definitions

door_obstacle_center_1[2,type=float] # Door environment, obstacle 1 center door_obstacle_size_1[2,type=float] # Door environment, obstacle 1 size door_obstacle_center_2[2,type=float] # Door environment, obstacle 2 center door_obstacle_size_2[2,type=float] # Door environment, obstacle 2 size

wall_obstacle_center[2,type=float] # Wall environment, obstacle center wall_obstacle_size[2,type=float] # Wall environment, obstacle size

combined_obstacle_center_1[2,type=float] # Combined environment, obstacle 1 center combined_obstacle_size_1[2,type=float] # Combined environment, obstacle 1 size combined_obstacle_center_2[2,type=float] # Combined environment, obstacle 2 center combined_obstacle_size_2[2,type=float] # Combined environment, obstacle 2 size combined_obstacle_center_3[2,type=float] # Combined environment, obstacle 3 center combined_obstacle_size_3[2,type=float] # Combined environment, obstacle 3 size

Agent configurations

agent1_id[1,type=int] # Agent 1 ID agent1_radius[1,type=float] # Agent 1 radius agent1_initial_position[2,type=float] # Agent 1 initial position agent1_target_position[2,type=float] # Agent 1 target position

agent2_id[1,type=int] # Agent 2 ID agent2_radius[1,type=float] # Agent 2 radius agent2_initial_position[2,type=float] # Agent 2 initial position agent2_target_position[2,type=float] # Agent 2 target position

agent3_id[1,type=int] # Agent 3 ID agent3_radius[1,type=float] # Agent 3 radius agent3_initial_position[2,type=float] # Agent 3 initial position agent3_target_position[2,type=float] # Agent 3 target position

agent4_id[1,type=int] # Agent 4 ID agent4_radius[1,type=float] # Agent 4 radius agent4_initial_position[2,type=float] # Agent 4 initial position agent4_target_position[2,type=float] # Agent 4 target position

Experiment configurations

experiment_seeds[2,type=int] # Random seeds for reproducibility results_dir[1,type=string] # Base directory for results animation_template[1,type=string] # Filename template for animations control_vis_filename[1,type=string] # Filename for control visualization obstacle_distance_filename[1,type=string] # Filename for obstacle distance plot path_uncertainty_filename[1,type=string] # Filename for path uncertainty plot convergence_filename[1,type=string] # Filename for convergence plot

Connections

Model parameters

dt > A (A, B, C) > state_space_model

Agent trajectories

(state_space_model, initial_state_variance, control_variance) > agent_trajectories

Goal constraints

(agent_trajectories, goal_constraint_variance) > goal_directed_behavior

Obstacle avoidance

(agent_trajectories, gamma, gamma_shape, gamma_scale_factor) > obstacle_avoidance

Collision avoidance

(agent_trajectories, nr_agents) > collision_avoidance

Complete planning system

(goal_directed_behavior, obstacle_avoidance, collision_avoidance) > planning_system

InitialParameterization

Model parameters

dt=1.0 gamma=1.0 nr_steps=40 nr_iterations=350 nr_agents=4 softmin_temperature=10.0 intermediate_steps=10 save_intermediates=false

State space matrices

A = [1 dt 0 0; 0 1 0 0; 0 0 1 dt; 0 0 0 1]

A={(1.0, 1.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.0, 0.0, 1.0)}

B = [0 0; dt 0; 0 0; 0 dt]

B={(0.0, 0.0), (1.0, 0.0), (0.0, 0.0), (0.0, 1.0)}

C = [1 0 0 0; 0 0 1 0]

C={(1.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)}

Prior distributions

initial_state_variance=100.0 control_variance=0.1 goal_constraint_variance=0.00001 gamma_shape=1.5 gamma_scale_factor=0.5

Visualization parameters

x_limits={(-20, 20)} y_limits={(-20, 20)} fps=15 heatmap_resolution=100 plot_width=800 plot_height=400 agent_alpha=1.0 target_alpha=0.2 color_palette="tab10"

Environment definitions

door_obstacle_center_1={(-40.0, 0.0)} door_obstacle_size_1={(70.0, 5.0)} door_obstacle_center_2={(40.0, 0.0)} door_obstacle_size_2={(70.0, 5.0)}

wall_obstacle_center={(0.0, 0.0)} wall_obstacle_size={(10.0, 5.0)}

combined_obstacle_center_1={(-50.0, 0.0)} combined_obstacle_size_1={(70.0, 2.0)} combined_obstacle_center_2={(50.0, 0.0)} combined_obstacle_size_2={(70.0, 2.0)} combined_obstacle_center_3={(5.0, -1.0)} combined_obstacle_size_3={(3.0, 10.0)}

Agent configurations

agent1_id=1 agent1_radius=2.5 agent1_initial_position={(-4.0, 10.0)} agent1_target_position={(-10.0, -10.0)}

agent2_id=2 agent2_radius=1.5 agent2_initial_position={(-10.0, 5.0)} agent2_target_position={(10.0, -15.0)}

agent3_id=3 agent3_radius=1.0 agent3_initial_position={(-15.0, -10.0)} agent3_target_position={(10.0, 10.0)}

agent4_id=4 agent4_radius=2.5 agent4_initial_position={(0.0, -10.0)} agent4_target_position={(-10.0, 15.0)}

Experiment configurations

experiment_seeds={(42, 123)} results_dir="results" animation_template="{environment}_{seed}.gif" control_vis_filename="control_signals.gif" obstacle_distance_filename="obstacle_distance.png" path_uncertainty_filename="path_uncertainty.png" convergence_filename="convergence.png"

Equations

State space model:

x_{t+1} = A * x_t + B * u_t + w_t, w_t ~ N(0, control_variance)

y_t = C * x_t + v_t, v_t ~ N(0, observation_variance)

Obstacle avoidance constraint:

p(x_t | obstacle) ~ N(d(x_t, obstacle), gamma)

where d(x_t, obstacle) is the distance from position x_t to the nearest obstacle

Goal constraint:

p(x_T | goal) ~ N(goal, goal_constraint_variance)

where x_T is the final position

Collision avoidance constraint:

p(x_i, x_j) ~ N(||x_i - x_j|| - (r_i + r_j), gamma)

where x_i, x_j are positions of agents i and j, r_i, r_j are their radii

Time

Dynamic DiscreteTime ModelTimeHorizon=nr_steps

ActInfOntologyAnnotation

dt=TimeStep gamma=ConstraintParameter nr_steps=TrajectoryLength nr_iterations=InferenceIterations nr_agents=NumberOfAgents softmin_temperature=SoftminTemperature A=StateTransitionMatrix B=ControlInputMatrix C=ObservationMatrix initial_state_variance=InitialStateVariance control_variance=ControlVariance goal_constraint_variance=GoalConstraintVariance

ModelParameters

nr_agents=4 nr_steps=40 nr_iterations=350

Footer

Multi-agent Trajectory Planning - GNN Representation for RxInfer.jl

Signature

Creator: AI Assistant for GNN Date: 2024-07-27 Status: Example for RxInfer.jl multi-agent trajectory planning \n```\n\n## Parsed Sections

_HeaderComments

# GNN Example: RxInfer Multi-agent Trajectory Planning
# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl
# Version: 1.0
# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.

ModelName

Multi-agent Trajectory Planning

GNNSection

RxInferMultiAgentTrajectoryPlanning

GNNVersionAndFlags

GNN v1

ModelAnnotation

This model represents a multi-agent trajectory planning scenario in RxInfer.jl.
It includes:
- State space model for agents moving in a 2D environment
- Obstacle avoidance constraints
- Goal-directed behavior
- Inter-agent collision avoidance
The model can be used to simulate trajectory planning in various environments with obstacles.

StateSpaceBlock

# Model parameters
dt[1,type=float]               # Time step for the state space model
gamma[1,type=float]            # Constraint parameter for the Halfspace node
nr_steps[1,type=int]           # Number of time steps in the trajectory
nr_iterations[1,type=int]      # Number of inference iterations
nr_agents[1,type=int]          # Number of agents in the simulation
softmin_temperature[1,type=float] # Temperature parameter for the softmin function
intermediate_steps[1,type=int] # Intermediate results saving interval
save_intermediates[1,type=bool] # Whether to save intermediate results

# State space matrices
A[4,4,type=float]              # State transition matrix
B[4,2,type=float]              # Control input matrix
C[2,4,type=float]              # Observation matrix

# Prior distributions
initial_state_variance[1,type=float]    # Prior on initial state
control_variance[1,type=float]          # Prior on control inputs
goal_constraint_variance[1,type=float]  # Goal constraints variance
gamma_shape[1,type=float]               # Parameters for GammaShapeRate prior
gamma_scale_factor[1,type=float]        # Parameters for GammaShapeRate prior

# Visualization parameters
x_limits[2,type=float]            # Plot boundaries (x-axis)
y_limits[2,type=float]            # Plot boundaries (y-axis)
fps[1,type=int]                   # Animation frames per second
heatmap_resolution[1,type=int]    # Heatmap resolution
plot_width[1,type=int]            # Plot width
plot_height[1,type=int]           # Plot height
agent_alpha[1,type=float]         # Visualization alpha for agents
target_alpha[1,type=float]        # Visualization alpha for targets
color_palette[1,type=string]      # Color palette for visualization

# Environment definitions
door_obstacle_center_1[2,type=float]    # Door environment, obstacle 1 center
door_obstacle_size_1[2,type=float]      # Door environment, obstacle 1 size
door_obstacle_center_2[2,type=float]    # Door environment, obstacle 2 center
door_obstacle_size_2[2,type=float]      # Door environment, obstacle 2 size

wall_obstacle_center[2,type=float]      # Wall environment, obstacle center
wall_obstacle_size[2,type=float]        # Wall environment, obstacle size

combined_obstacle_center_1[2,type=float] # Combined environment, obstacle 1 center
combined_obstacle_size_1[2,type=float]   # Combined environment, obstacle 1 size
combined_obstacle_center_2[2,type=float] # Combined environment, obstacle 2 center
combined_obstacle_size_2[2,type=float]   # Combined environment, obstacle 2 size
combined_obstacle_center_3[2,type=float] # Combined environment, obstacle 3 center
combined_obstacle_size_3[2,type=float]   # Combined environment, obstacle 3 size

# Agent configurations
agent1_id[1,type=int]                   # Agent 1 ID
agent1_radius[1,type=float]             # Agent 1 radius
agent1_initial_position[2,type=float]   # Agent 1 initial position
agent1_target_position[2,type=float]    # Agent 1 target position

agent2_id[1,type=int]                   # Agent 2 ID
agent2_radius[1,type=float]             # Agent 2 radius
agent2_initial_position[2,type=float]   # Agent 2 initial position
agent2_target_position[2,type=float]    # Agent 2 target position

agent3_id[1,type=int]                   # Agent 3 ID
agent3_radius[1,type=float]             # Agent 3 radius
agent3_initial_position[2,type=float]   # Agent 3 initial position
agent3_target_position[2,type=float]    # Agent 3 target position

agent4_id[1,type=int]                   # Agent 4 ID
agent4_radius[1,type=float]             # Agent 4 radius
agent4_initial_position[2,type=float]   # Agent 4 initial position
agent4_target_position[2,type=float]    # Agent 4 target position

# Experiment configurations
experiment_seeds[2,type=int]            # Random seeds for reproducibility
results_dir[1,type=string]              # Base directory for results
animation_template[1,type=string]       # Filename template for animations
control_vis_filename[1,type=string]     # Filename for control visualization
obstacle_distance_filename[1,type=string] # Filename for obstacle distance plot
path_uncertainty_filename[1,type=string]  # Filename for path uncertainty plot
convergence_filename[1,type=string]       # Filename for convergence plot

Connections

# Model parameters
dt > A
(A, B, C) > state_space_model

# Agent trajectories
(state_space_model, initial_state_variance, control_variance) > agent_trajectories

# Goal constraints
(agent_trajectories, goal_constraint_variance) > goal_directed_behavior

# Obstacle avoidance
(agent_trajectories, gamma, gamma_shape, gamma_scale_factor) > obstacle_avoidance

# Collision avoidance
(agent_trajectories, nr_agents) > collision_avoidance

# Complete planning system
(goal_directed_behavior, obstacle_avoidance, collision_avoidance) > planning_system

InitialParameterization

# Model parameters
dt=1.0
gamma=1.0
nr_steps=40
nr_iterations=350
nr_agents=4
softmin_temperature=10.0
intermediate_steps=10
save_intermediates=false

# State space matrices
# A = [1 dt 0 0; 0 1 0 0; 0 0 1 dt; 0 0 0 1]
A={(1.0, 1.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.0, 0.0, 1.0)}

# B = [0 0; dt 0; 0 0; 0 dt]
B={(0.0, 0.0), (1.0, 0.0), (0.0, 0.0), (0.0, 1.0)}

# C = [1 0 0 0; 0 0 1 0]
C={(1.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)}

# Prior distributions
initial_state_variance=100.0
control_variance=0.1
goal_constraint_variance=0.00001
gamma_shape=1.5
gamma_scale_factor=0.5

# Visualization parameters
x_limits={(-20, 20)}
y_limits={(-20, 20)}
fps=15
heatmap_resolution=100
plot_width=800
plot_height=400
agent_alpha=1.0
target_alpha=0.2
color_palette="tab10"

# Environment definitions
door_obstacle_center_1={(-40.0, 0.0)}
door_obstacle_size_1={(70.0, 5.0)}
door_obstacle_center_2={(40.0, 0.0)}
door_obstacle_size_2={(70.0, 5.0)}

wall_obstacle_center={(0.0, 0.0)}
wall_obstacle_size={(10.0, 5.0)}

combined_obstacle_center_1={(-50.0, 0.0)}
combined_obstacle_size_1={(70.0, 2.0)}
combined_obstacle_center_2={(50.0, 0.0)}
combined_obstacle_size_2={(70.0, 2.0)}
combined_obstacle_center_3={(5.0, -1.0)}
combined_obstacle_size_3={(3.0, 10.0)}

# Agent configurations
agent1_id=1
agent1_radius=2.5
agent1_initial_position={(-4.0, 10.0)}
agent1_target_position={(-10.0, -10.0)}

agent2_id=2
agent2_radius=1.5
agent2_initial_position={(-10.0, 5.0)}
agent2_target_position={(10.0, -15.0)}

agent3_id=3
agent3_radius=1.0
agent3_initial_position={(-15.0, -10.0)}
agent3_target_position={(10.0, 10.0)}

agent4_id=4
agent4_radius=2.5
agent4_initial_position={(0.0, -10.0)}
agent4_target_position={(-10.0, 15.0)}

# Experiment configurations
experiment_seeds={(42, 123)}
results_dir="results"
animation_template="{environment}_{seed}.gif"
control_vis_filename="control_signals.gif"
obstacle_distance_filename="obstacle_distance.png"
path_uncertainty_filename="path_uncertainty.png"
convergence_filename="convergence.png"

Equations

# State space model:
# x_{t+1} = A * x_t + B * u_t + w_t,  w_t ~ N(0, control_variance)
# y_t = C * x_t + v_t,                v_t ~ N(0, observation_variance)
#
# Obstacle avoidance constraint:
# p(x_t | obstacle) ~ N(d(x_t, obstacle), gamma)
# where d(x_t, obstacle) is the distance from position x_t to the nearest obstacle
#
# Goal constraint:
# p(x_T | goal) ~ N(goal, goal_constraint_variance)
# where x_T is the final position
#
# Collision avoidance constraint:
# p(x_i, x_j) ~ N(||x_i - x_j|| - (r_i + r_j), gamma)
# where x_i, x_j are positions of agents i and j, r_i, r_j are their radii

Time

Dynamic
DiscreteTime
ModelTimeHorizon=nr_steps

ActInfOntologyAnnotation

dt=TimeStep
gamma=ConstraintParameter
nr_steps=TrajectoryLength
nr_iterations=InferenceIterations
nr_agents=NumberOfAgents
softmin_temperature=SoftminTemperature
A=StateTransitionMatrix
B=ControlInputMatrix
C=ObservationMatrix
initial_state_variance=InitialStateVariance
control_variance=ControlVariance
goal_constraint_variance=GoalConstraintVariance

ModelParameters

nr_agents=4
nr_steps=40
nr_iterations=350

Footer

Multi-agent Trajectory Planning - GNN Representation for RxInfer.jl

Signature

Creator: AI Assistant for GNN
Date: 2024-07-27
Status: Example for RxInfer.jl multi-agent trajectory planning

JSON Files

full_model_data.json

{
  "_HeaderComments": "# GNN Example: RxInfer Multi-agent Trajectory Planning\n# Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl\n# Version: 1.0\n# This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.",
  "ModelName": "Multi-agent Trajectory Planning",
  "GNNSection": "RxInferMultiAgentTrajectoryPlanning",
  "GNNVersionAndFlags": "GNN v1",
  "ModelAnnotation": "This model represents a multi-agent trajectory planning scenario in RxInfer.jl.\nIt includes:\n- State space model for agents moving in a 2D environment\n- Obstacle avoidance constraints\n- Goal-directed behavior\n- Inter-agent collision avoidance\nThe model can be used to simulate trajectory planning in various environments with obstacles.",
  "StateSpaceBlock": "# Model parameters\ndt[1,type=float]               # Time step for the state space model\ngamma[1,type=float]            # Constraint parameter for the Halfspace node\nnr_steps[1,type=int]           # Number of time steps in the trajectory\nnr_iterations[1,type=int]      # Number of inference iterations\nnr_agents[1,type=int]          # Number of agents in the simulation\nsoftmin_temperature[1,type=float] # Temperature parameter for the softmin function\nintermediate_steps[1,type=int] # Intermediate results saving interval\nsave_intermediates[1,type=bool] # Whether to save intermediate results\n\n# State space matrices\nA[4,4,type=float]              # State transition matrix\nB[4,2,type=float]              # Control input matrix\nC[2,4,type=float]              # Observation matrix\n\n# Prior distributions\ninitial_state_variance[1,type=float]    # Prior on initial state\ncontrol_variance[1,type=float]          # Prior on control inputs\ngoal_constraint_variance[1,type=float]  # Goal constraints variance\ngamma_shape[1,type=float]               # Parameters for GammaShapeRate prior\ngamma_scale_factor[1,type=float]        # Parameters for GammaShapeRate prior\n\n# Visualization parameters\nx_limits[2,type=float]            # Plot boundaries (x-axis)\ny_limits[2,type=float]            # Plot boundaries (y-axis)\nfps[1,type=int]                   # Animation frames per second\nheatmap_resolution[1,type=int]    # Heatmap resolution\nplot_width[1,type=int]            # Plot width\nplot_height[1,type=int]           # Plot height\nagent_alpha[1,type=float]         # Visualization alpha for agents\ntarget_alpha[1,type=float]        # Visualization alpha for targets\ncolor_palette[1,type=string]      # Color palette for visualization\n\n# Environment definitions\ndoor_obstacle_center_1[2,type=float]    # Door environment, obstacle 1 center\ndoor_obstacle_size_1[2,type=float]      # Door environment, obstacle 1 size\ndoor_obstacle_center_2[2,type=float]    # Door environment, obstacle 2 center\ndoor_obstacle_size_2[2,type=float]      # Door environment, obstacle 2 size\n\nwall_obstacle_center[2,type=float]      # Wall environment, obstacle center\nwall_obstacle_size[2,type=float]        # Wall environment, obstacle size\n\ncombined_obstacle_center_1[2,type=float] # Combined environment, obstacle 1 center\ncombined_obstacle_size_1[2,type=float]   # Combined environment, obstacle 1 size\ncombined_obstacle_center_2[2,type=float] # Combined environment, obstacle 2 center\ncombined_obstacle_size_2[2,type=float]   # Combined environment, obstacle 2 size\ncombined_obstacle_center_3[2,type=float] # Combined environment, obstacle 3 center\ncombined_obstacle_size_3[2,type=float]   # Combined environment, obstacle 3 size\n\n# Agent configurations\nagent1_id[1,type=int]                   # Agent 1 ID\nagent1_radius[1,type=float]             # Agent 1 radius\nagent1_initial_position[2,type=float]   # Agent 1 initial position\nagent1_target_position[2,type=float]    # Agent 1 target position\n\nagent2_id[1,type=int]                   # Agent 2 ID\nagent2_radius[1,type=float]             # Agent 2 radius\nagent2_initial_position[2,type=float]   # Agent 2 initial position\nagent2_target_position[2,type=float]    # Agent 2 target position\n\nagent3_id[1,type=int]                   # Agent 3 ID\nagent3_radius[1,type=float]             # Agent 3 radius\nagent3_initial_position[2,type=float]   # Agent 3 initial position\nagent3_target_position[2,type=float]    # Agent 3 target position\n\nagent4_id[1,type=int]                   # Agent 4 ID\nagent4_radius[1,type=float]             # Agent 4 radius\nagent4_initial_position[2,type=float]   # Agent 4 initial position\nagent4_target_position[2,type=float]    # Agent 4 target position\n\n# Experiment configurations\nexperiment_seeds[2,type=int]            # Random seeds for reproducibility\nresults_dir[1,type=string]              # Base directory for results\nanimation_template[1,type=string]       # Filename template for animations\ncontrol_vis_filename[1,type=string]     # Filename for control visualization\nobstacle_distance_filename[1,type=string] # Filename for obstacle distance plot\npath_uncertainty_filename[1,type=string]  # Filename for path uncertainty plot\nconvergence_filename[1,type=string]       # Filename for convergence plot",
  "Connections": "# Model parameters\ndt > A\n(A, B, C) > state_space_model\n\n# Agent trajectories\n(state_space_model, initial_state_variance, control_variance) > agent_trajectories\n\n# Goal constraints\n(agent_trajectories, goal_constraint_variance) > goal_directed_behavior\n\n# Obstacle avoidance\n(agent_trajectories, gamma, gamma_shape, gamma_scale_factor) > obstacle_avoidance\n\n# Collision avoidance\n(agent_trajectories, nr_agents) > collision_avoidance\n\n# Complete planning system\n(goal_directed_behavior, obstacle_avoidance, collision_avoidance) > planning_system",
  "InitialParameterization": "# Model parameters\ndt=1.0\ngamma=1.0\nnr_steps=40\nnr_iterations=350\nnr_agents=4\nsoftmin_temperature=10.0\nintermediate_steps=10\nsave_intermediates=false\n\n# State space matrices\n# A = [1 dt 0 0; 0 1 0 0; 0 0 1 dt; 0 0 0 1]\nA={(1.0, 1.0, 0.0, 0.0), (0.0, 1.0, 0.0, 0.0), (0.0, 0.0, 1.0, 1.0), (0.0, 0.0, 0.0, 1.0)}\n\n# B = [0 0; dt 0; 0 0; 0 dt]\nB={(0.0, 0.0), (1.0, 0.0), (0.0, 0.0), (0.0, 1.0)}\n\n# C = [1 0 0 0; 0 0 1 0]\nC={(1.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)}\n\n# Prior distributions\ninitial_state_variance=100.0\ncontrol_variance=0.1\ngoal_constraint_variance=0.00001\ngamma_shape=1.5\ngamma_scale_factor=0.5\n\n# Visualization parameters\nx_limits={(-20, 20)}\ny_limits={(-20, 20)}\nfps=15\nheatmap_resolution=100\nplot_width=800\nplot_height=400\nagent_alpha=1.0\ntarget_alpha=0.2\ncolor_palette=\"tab10\"\n\n# Environment definitions\ndoor_obstacle_center_1={(-40.0, 0.0)}\ndoor_obstacle_size_1={(70.0, 5.0)}\ndoor_obstacle_center_2={(40.0, 0.0)}\ndoor_obstacle_size_2={(70.0, 5.0)}\n\nwall_obstacle_center={(0.0, 0.0)}\nwall_obstacle_size={(10.0, 5.0)}\n\ncombined_obstacle_center_1={(-50.0, 0.0)}\ncombined_obstacle_size_1={(70.0, 2.0)}\ncombined_obstacle_center_2={(50.0, 0.0)}\ncombined_obstacle_size_2={(70.0, 2.0)}\ncombined_obstacle_center_3={(5.0, -1.0)}\ncombined_obstacle_size_3={(3.0, 10.0)}\n\n# Agent configurations\nagent1_id=1\nagent1_radius=2.5\nagent1_initial_position={(-4.0, 10.0)}\nagent1_target_position={(-10.0, -10.0)}\n\nagent2_id=2\nagent2_radius=1.5\nagent2_initial_position={(-10.0, 5.0)}\nagent2_target_position={(10.0, -15.0)}\n\nagent3_id=3\nagent3_radius=1.0\nagent3_initial_position={(-15.0, -10.0)}\nagent3_target_position={(10.0, 10.0)}\n\nagent4_id=4\nagent4_radius=2.5\nagent4_initial_position={(0.0, -10.0)}\nagent4_target_position={(-10.0, 15.0)}\n\n# Experiment configurations\nexperiment_seeds={(42, 123)}\nresults_dir=\"results\"\nanimation_template=\"{environment}_{seed}.gif\"\ncontrol_vis_filename=\"control_signals.gif\"\nobstacle_distance_filename=\"obstacle_distance.png\"\npath_uncertainty_filename=\"path_uncertainty.png\"\nconvergence_filename=\"convergence.png\"",
  "Equations": "# State space model:\n# x_{t+1} = A * x_t + B * u_t + w_t,  w_t ~ N(0, control_variance)\n# y_t = C * x_t + v_t,                v_t ~ N(0, observation_variance)\n#\n# Obstacle avoidance constraint:\n# p(x_t | obstacle) ~ N(d(x_t, obstacle), gamma)\n# where d(x_t, obstacle) is the distance from position x_t to the nearest obstacle\n#\n# Goal constraint:\n# p(x_T | goal) ~ N(goal, goal_constraint_variance)\n# where x_T is the final position\n#\n# Collision avoidance constraint:\n# p(x_i, x_j) ~ N(||x_i - x_j|| - (r_i + r_j), gamma)\n# where x_i, x_j are positions of agents i and j, r_i, r_j are their radii",
  "Time": "Dynamic\nDiscreteTime\nModelTimeHorizon=nr_steps",
  "ActInfOntologyAnnotation": "dt=TimeStep\ngamma=ConstraintParameter\nnr_steps=TrajectoryLength\nnr_iterations=InferenceIterations\nnr_agents=NumberOfAgents\nsoftmin_temperature=SoftminTemperature\nA=StateTransitionMatrix\nB=ControlInputMatrix\nC=ObservationMatrix\ninitial_state_variance=InitialStateVariance\ncontrol_variance=ControlVariance\ngoal_constraint_variance=GoalConstraintVariance",
  "ModelParameters": "nr_agents=4\nnr_steps=40\nnr_iterations=350",
  "Footer": "Multi-agent Trajectory Planning - GNN Representation for RxInfer.jl",
  "Signature": "Creator: AI Assistant for GNN\nDate: 2024-07-27\nStatus: Example for RxInfer.jl multi-agent trajectory planning"
}

full_model_data.json

model_metadata.json

{
  "ModelName": "Multi-agent Trajectory Planning",
  "ModelAnnotation": "This model represents a multi-agent trajectory planning scenario in RxInfer.jl.\nIt includes:\n- State space model for agents moving in a 2D environment\n- Obstacle avoidance constraints\n- Goal-directed behavior\n- Inter-agent collision avoidance\nThe model can be used to simulate trajectory planning in various environments with obstacles.",
  "GNNVersionAndFlags": "GNN v1",
  "Time": "Dynamic\nDiscreteTime\nModelTimeHorizon=nr_steps",
  "ActInfOntologyAnnotation": "dt=TimeStep\ngamma=ConstraintParameter\nnr_steps=TrajectoryLength\nnr_iterations=InferenceIterations\nnr_agents=NumberOfAgents\nsoftmin_temperature=SoftminTemperature\nA=StateTransitionMatrix\nB=ControlInputMatrix\nC=ObservationMatrix\ninitial_state_variance=InitialStateVariance\ncontrol_variance=ControlVariance\ngoal_constraint_variance=GoalConstraintVariance"
}

model_metadata.json

MCP Integration Report (Step 7)

🤖 MCP Integration and API Report

🗓️ Report Generated: 2025-06-06 13:41:45

MCP Core Directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/mcp Project Source Root (for modules): /home/trim/Documents/GitHub/GeneralizedNotationNotation/src Output Directory for this report: /home/trim/Documents/GitHub/GeneralizedNotationNotation/output/mcp_processing_step

🌐 Global Summary of Registered MCP Tools

This section lists all tools currently registered with the MCP system, along with their defining module, arguments, and description.

Tool: ensure_directory_exists
Defined in Module: src.setup.mcp
Arguments (from signature): (directory_path)
Description: "Ensures a directory exists, creating it if necessary. Returns the absolute path."
Schema: json { "directory_path": { "type": "string", "description": "Path of the directory to create if it doesn't exist." } }
Tool: estimate_resources_for_gnn_directory
Defined in Module: src.gnn_type_checker.mcp
Arguments (from signature): (dir_path, recursive)
Description: "Estimates computational resources for all GNN files in a specified directory."
Schema: json { "dir_path": { "type": "string", "description": "Path to the directory for GNN resource estimation." }, "recursive": { "type": "boolean", "description": "Search directory recursively. Defaults to False.", "optional": true } }
Tool: estimate_resources_for_gnn_file
Defined in Module: src.gnn_type_checker.mcp
Arguments (from signature): (file_path)
Description: "Estimates computational resources (memory, inference, storage) for a GNN model file."
Schema: json { "file_path": { "type": "string", "description": "Path to the GNN file for resource estimation." } }
Tool: export_gnn_to_gexf
Defined in Module: src.export.mcp
Arguments (from signature): (gnn_file_path, output_file_path)
Description: "Exports a GNN model to GEXF graph format (requires NetworkX)."
Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
Tool: export_gnn_to_graphml
Defined in Module: src.export.mcp
Arguments (from signature): (gnn_file_path, output_file_path)
Description: "Exports a GNN model to GraphML graph format (requires NetworkX)."
Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
Tool: export_gnn_to_json
Defined in Module: src.export.mcp
Arguments (from signature): (gnn_file_path, output_file_path)
Description: "Exports a GNN model to JSON format."
Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
Tool: export_gnn_to_json_adjacency_list
Defined in Module: src.export.mcp
Arguments (from signature): (gnn_file_path, output_file_path)
Description: "Exports a GNN model to JSON Adjacency List graph format (requires NetworkX)."
Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
Tool: export_gnn_to_plaintext_dsl
Defined in Module: src.export.mcp
Arguments (from signature): (gnn_file_path, output_file_path)
Description: "Exports a GNN model back to its GNN DSL plain text format."
Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
Tool: export_gnn_to_plaintext_summary
Defined in Module: src.export.mcp
Arguments (from signature): (gnn_file_path, output_file_path)
Description: "Exports a GNN model to a human-readable plain text summary."
Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
Tool: export_gnn_to_python_pickle
Defined in Module: src.export.mcp
Arguments (from signature): (gnn_file_path, output_file_path)
Description: "Serializes a GNN model to a Python pickle file."
Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
Tool: export_gnn_to_xml
Defined in Module: src.export.mcp
Arguments (from signature): (gnn_file_path, output_file_path)
Description: "Exports a GNN model to XML format."
Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
Tool: find_project_gnn_files
Defined in Module: src.setup.mcp
Arguments (from signature): (search_directory, recursive)
Description: "Finds all GNN (.md) files in a specified directory within the project."
Schema: json { "search_directory": { "type": "string", "description": "The directory to search for GNN (.md) files." }, "recursive": { "type": "boolean", "description": "Set to true to search recursively. Defaults to false.", "optional": true } }
Tool: generate_pipeline_summary_site
Defined in Module: src.site.mcp
Arguments (from signature): (output_dir, site_output_filename, verbose)
Description: "Generates a single HTML website summarizing all contents of the GNN pipeline output directory."
Schema: json { "type": "object", "properties": { "output_dir": { "type": "string", "description": "The main pipeline output directory to scan for results." }, "site_output_filename": { "type": "string", "description": "The filename for the output HTML report (e.g., 'summary.html')." }, "verbose": { "type": "boolean", "description": "Enable verbose logging for the generator." } }, "required": [ "output_dir", "site_output_filename" ] }
Tool: get_gnn_documentation
Defined in Module: src.gnn.mcp
Arguments (from signature): (doc_name)
Description: "Retrieve the content of a GNN core documentation file (e.g., syntax, file structure)."
Schema: json { "doc_name": { "type": "string", "description": "Name of the GNN document (e.g., 'file_structure', 'punctuation')", "enum": [ "file_structure", "punctuation" ] } }
Tool: get_standard_output_paths
Defined in Module: src.setup.mcp
Arguments (from signature): (base_output_directory)
Description: "Gets a dictionary of standard output directory paths (e.g., for type_check, visualization), creating them if needed."
Schema: json { "base_output_directory": { "type": "string", "description": "The base directory where output subdirectories will be managed." } }
Tool: list_render_targets
Defined in Module: src.render.mcp
Arguments (from signature): ()
Description: "Lists the available target formats for GNN rendering (e.g., pymdp, rxinfer)."
Schema: json { "properties": {}, "title": "ListRenderTargetsInput", "type": "object" }
Tool: llm.explain_gnn_file
Defined in Module: src.llm.mcp
Arguments (from signature): (file_path_str, aspect_to_explain)
Description: "Reads a GNN specification file and uses an LLM to generate an explanation of its content. Can focus on a specific aspect if provided."
Schema: json { "type": "object", "properties": { "file_path_str": { "type": "string", "description": "The absolute or relative path to the GNN file." }, "aspect_to_explain": { "type": "string", "description": "(Optional) A specific part or concept within the GNN to focus the explanation on." } }, "required": [ "file_path_str" ] }
Tool: llm.generate_professional_summary
Defined in Module: src.llm.mcp
Arguments (from signature): (file_path_str, experiment_details, target_audience)
Description: "Reads a GNN file and optional experiment details, then uses an LLM to generate a professional summary suitable for reports or papers."
Schema: json { "type": "object", "properties": { "file_path_str": { "type": "string", "description": "The absolute or relative path to the GNN file." }, "experiment_details": { "type": "string", "description": "(Optional) Text describing the experiments conducted with the model, including setup, results, or observations." }, "target_audience": { "type": "string", "description": "(Optional) The intended audience for the summary (e.g., 'fellow researchers', 'project managers'). Default: 'fellow researchers'." } }, "required": [ "file_path_str" ] }
Tool: llm.summarize_gnn_file
Defined in Module: src.llm.mcp
Arguments (from signature): (file_path_str, user_prompt_suffix)
Description: "Reads a GNN specification file and uses an LLM to generate a concise summary of its content. Optionally, a user prompt suffix can refine the summary focus."
Schema: json { "type": "object", "properties": { "file_path_str": { "type": "string", "description": "The absolute or relative path to the GNN file (.md, .gnn.md, .json)." }, "user_prompt_suffix": { "type": "string", "description": "(Optional) Additional instructions or focus points for the summary." } }, "required": [ "file_path_str" ] }
Tool: parse_gnn_file
Defined in Module: src.visualization.mcp
Arguments (from signature): (file_path)
Description: "Parse a GNN file without visualization"
Schema: json { "file_path": { "type": "string", "description": "Path to the GNN file to parse" } }
Tool: render_gnn_specification
Defined in Module: src.render.mcp
Arguments (from signature): (input_data)
Description: "Renders a GNN (Generalized Notation Notation) specification into an executable format for a target modeling environment like PyMDP or RxInfer.jl."
Schema: json { "properties": { "gnn_specification": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "string" } ], "description": "The GNN specification itself as a dictionary, or a string URI/path to a GNN spec file (e.g., JSON).", "title": "Gnn Specification" }, "target_format": { "description": "The target format to render the GNN specification to.", "enum": [ "pymdp", "rxinfer" ], "title": "Target Format", "type": "string" }, "output_filename_base": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Optional desired base name for the output file (e.g., 'my_model'). Extension is added automatically. If None, derived from GNN spec name or input file name.", "title": "Output Filename Base" }, "render_options": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "default": null, "description": "Optional dictionary of specific options for the chosen renderer (e.g., data_bindings for RxInfer).", "title": "Render Options" } }, "required": [ "gnn_specification", "target_format" ], "title": "RenderGnnInput", "type": "object" }
Tool: run_gnn_type_checker
Defined in Module: src.tests.mcp
Arguments (from signature): (file_path)
Description: "Run the GNN type checker on a specific file (via test module)."
Schema: json { "file_path": { "type": "string", "description": "Path to the GNN file to check" } }
Tool: run_gnn_type_checker_on_directory
Defined in Module: src.tests.mcp
Arguments (from signature): (dir_path, report_file)
Description: "Run the GNN type checker on all GNN files in a directory (via test module)."
Schema: json { "dir_path": { "type": "string", "description": "Path to directory containing GNN files" }, "report_file": { "type": "string", "description": "Optional path to save the report" } }
Tool: run_gnn_unit_tests
Defined in Module: src.tests.mcp
Arguments (from signature): ()
Description: "Run the GNN unit tests and return results."
Schema: json No schema provided.
Tool: sympy_analyze_stability
Defined in Module: src.mcp.sympy_mcp
Arguments (from signature): (transition_matrices)
Description: "Analyze system stability using eigenvalue analysis"
Schema: json { "type": "object", "properties": { "transition_matrices": { "type": "array", "description": "List of transition matrices to analyze" } }, "required": [ "transition_matrices" ] }
Tool: sympy_cleanup
Defined in Module: src.mcp.sympy_mcp
Arguments (from signature): ()
Description: "Clean up SymPy MCP integration and reset state"
Schema: json { "type": "object", "properties": {} }
Tool: sympy_get_latex
Defined in Module: src.mcp.sympy_mcp
Arguments (from signature): (expression)
Description: "Convert a mathematical expression to LaTeX format"
Schema: json { "type": "object", "properties": { "expression": { "type": "string", "description": "Expression to convert to LaTeX" } }, "required": [ "expression" ] }
Tool: sympy_initialize
Defined in Module: src.mcp.sympy_mcp
Arguments (from signature): (server_executable)
Description: "Initialize SymPy MCP integration"
Schema: json { "type": "object", "properties": { "server_executable": { "type": "string", "description": "Path to SymPy MCP server executable", "default": null } } }
Tool: sympy_simplify_expression
Defined in Module: src.mcp.sympy_mcp
Arguments (from signature): (expression)
Description: "Simplify a mathematical expression to canonical form"
Schema: json { "type": "object", "properties": { "expression": { "type": "string", "description": "Mathematical expression to simplify" } }, "required": [ "expression" ] }
Tool: sympy_solve_equation
Defined in Module: src.mcp.sympy_mcp
Arguments (from signature): (equation, variable, domain)
Description: "Solve an equation algebraically for a specified variable"
Schema: json { "type": "object", "properties": { "equation": { "type": "string", "description": "Equation to solve" }, "variable": { "type": "string", "description": "Variable to solve for" }, "domain": { "type": "string", "description": "Solution domain (COMPLEX, REAL, etc.)", "default": "COMPLEX" } }, "required": [ "equation", "variable" ] }
Tool: sympy_validate_equation
Defined in Module: src.mcp.sympy_mcp
Arguments (from signature): (equation, context)
Description: "Validate a mathematical equation using SymPy symbolic processing"
Schema: json { "type": "object", "properties": { "equation": { "type": "string", "description": "Mathematical equation to validate" }, "context": { "type": "object", "description": "GNN context for variable definitions", "default": {} } }, "required": [ "equation" ] }
Tool: sympy_validate_matrix
Defined in Module: src.mcp.sympy_mcp
Arguments (from signature): (matrix_data, matrix_type)
Description: "Validate matrix properties including stochasticity constraints"
Schema: json { "type": "object", "properties": { "matrix_data": { "type": "array", "description": "Matrix data as array of arrays" }, "matrix_type": { "type": "string", "description": "Type of matrix (transition, observation, etc.)", "default": "transition" } }, "required": [ "matrix_data" ] }
Tool: type_check_gnn_directory
Defined in Module: src.gnn_type_checker.mcp
Arguments (from signature): (dir_path, recursive, output_dir_base, report_md_filename)
Description: "Runs the GNN type checker on all GNN files in a specified directory. If output_dir_base is provided, reports are generated."
Schema: json { "dir_path": { "type": "string", "description": "Path to the directory containing GNN files to be type-checked." }, "recursive": { "type": "boolean", "description": "Search directory recursively. Defaults to False.", "optional": true }, "output_dir_base": { "type": "string", "description": "Optional base directory to save the report and other artifacts (HTML, JSON).", "optional": true }, "report_md_filename": { "type": "string", "description": "Optional filename for the markdown report (e.g., 'my_report.md'). Defaults to 'type_check_report.md'.", "optional": true } }
Tool: type_check_gnn_file
Defined in Module: src.gnn_type_checker.mcp
Arguments (from signature): (file_path)
Description: "Runs the GNN type checker on a specified GNN model file."
Schema: json { "file_path": { "type": "string", "description": "Path to the GNN file to be type-checked." } }
Tool: visualize_gnn_directory
Defined in Module: src.visualization.mcp
Arguments (from signature): (dir_path, output_dir)
Description: "Visualize all GNN files in a directory"
Schema: json { "dir_path": { "type": "string", "description": "Path to directory containing GNN files" }, "output_dir": { "type": "string", "description": "Optional output directory" } }
Tool: visualize_gnn_file
Defined in Module: src.visualization.mcp
Arguments (from signature): (file_path, output_dir)
Description: "Generate visualizations for a specific GNN file."
Schema: json { "file_path": { "type": "string", "description": "Path to the GNN file to visualize" }, "output_dir": { "type": "string", "description": "Optional output directory" } }

🔬 Core MCP File Check

This section verifies the presence of essential MCP files in the core directory: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/mcp

✅ mcp.py: Found (20304 bytes)
✅ meta_mcp.py: Found (4954 bytes)
✅ cli.py: Found (4644 bytes)
✅ server_stdio.py: Found (7620 bytes)
✅ server_http.py: Found (7731 bytes)

Status: 5/5 core MCP files found. All core files seem present.

🧩 Functional Module MCP Integration & API Check

Checking for mcp.py in these subdirectories of /home/trim/Documents/GitHub/GeneralizedNotationNotation/src: ['export', 'gnn', 'gnn_type_checker', 'ontology', 'setup', 'tests', 'visualization', 'llm']

Module: `export` (at `/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/export`)

✅ mcp.py Status: Found (7976 bytes)
Exposed Methods & Tools:
def _handle_export(export_func, gnn_file_path, output_file_path, format_name, requires_nx) (AST parsed) - *"Generic helper to run an export function and handle common exceptions."
def export_gnn_to_gexf(gnn_file_path, output_file_path) - *Description: "Exports a GNN model to GEXF graph format (requires NetworkX)."
- Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
def export_gnn_to_gexf_mcp(gnn_file_path, output_file_path) (AST parsed)
def export_gnn_to_graphml(gnn_file_path, output_file_path) - *Description: "Exports a GNN model to GraphML graph format (requires NetworkX)."
- Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
def export_gnn_to_graphml_mcp(gnn_file_path, output_file_path) (AST parsed)
def export_gnn_to_json(gnn_file_path, output_file_path) - *Description: "Exports a GNN model to JSON format."
- Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
def export_gnn_to_json_adjacency_list(gnn_file_path, output_file_path) - *Description: "Exports a GNN model to JSON Adjacency List graph format (requires NetworkX)."
- Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
def export_gnn_to_json_adjacency_list_mcp(gnn_file_path, output_file_path) (AST parsed)
def export_gnn_to_json_mcp(gnn_file_path, output_file_path) (AST parsed)
def export_gnn_to_plaintext_dsl(gnn_file_path, output_file_path) - *Description: "Exports a GNN model back to its GNN DSL plain text format."
- Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
def export_gnn_to_plaintext_dsl_mcp(gnn_file_path, output_file_path) (AST parsed)
def export_gnn_to_plaintext_summary(gnn_file_path, output_file_path) - *Description: "Exports a GNN model to a human-readable plain text summary."
- Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
def export_gnn_to_plaintext_summary_mcp(gnn_file_path, output_file_path) (AST parsed)
def export_gnn_to_python_pickle(gnn_file_path, output_file_path) - *Description: "Serializes a GNN model to a Python pickle file."
- Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
def export_gnn_to_python_pickle_mcp(gnn_file_path, output_file_path) (AST parsed)
def export_gnn_to_xml(gnn_file_path, output_file_path) - *Description: "Exports a GNN model to XML format."
- Schema: json { "gnn_file_path": { "type": "string", "description": "Path to the input GNN Markdown file (.gnn.md)." }, "output_file_path": { "type": "string", "description": "Path where the exported file will be saved." } }
def export_gnn_to_xml_mcp(gnn_file_path, output_file_path) (AST parsed)
def register_tools(mcp_instance) (AST parsed) - *"Registers all GNN export tools with the MCP instance."

Module: `gnn` (at `/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn`)

✅ mcp.py Status: Found (4122 bytes)
Exposed Methods & Tools:
def _retrieve_gnn_doc_resource(uri) (AST parsed) - *"Retrieve GNN documentation resource by URI."
def get_gnn_documentation(doc_name) - *Description: "Retrieve the content of a GNN core documentation file (e.g., syntax, file structure)."
- Schema: json { "doc_name": { "type": "string", "description": "Name of the GNN document (e.g., 'file_structure', 'punctuation')", "enum": [ "file_structure", "punctuation" ] } }
def register_tools(mcp_instance) (AST parsed) - *"Register GNN documentation tools and resources with the MCP."

Module: `gnn_type_checker` (at `/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn_type_checker`)

✅ mcp.py Status: Found (10921 bytes)
Exposed Methods & Tools:
def estimate_resources_for_gnn_directory(dir_path, recursive) - *Description: "Estimates computational resources for all GNN files in a specified directory."
- Schema: json { "dir_path": { "type": "string", "description": "Path to the directory for GNN resource estimation." }, "recursive": { "type": "boolean", "description": "Search directory recursively. Defaults to False.", "optional": true } }
def estimate_resources_for_gnn_directory_mcp(dir_path, recursive) (AST parsed) - *"Estimate resources for all GNN files in a directory. Exposed via MCP."
def estimate_resources_for_gnn_file(file_path) - *Description: "Estimates computational resources (memory, inference, storage) for a GNN model file."
- Schema: json { "file_path": { "type": "string", "description": "Path to the GNN file for resource estimation." } }
def estimate_resources_for_gnn_file_mcp(file_path) (AST parsed) - *"Estimate computational resources for a single GNN file. Exposed via MCP."
def register_tools(mcp_instance) (AST parsed) - *"Register GNN type checker and resource estimator tools with the MCP."
def type_check_gnn_directory(dir_path, recursive, output_dir_base, report_md_filename) - *Description: "Runs the GNN type checker on all GNN files in a specified directory. If output_dir_base is provided, reports are generated."
- Schema: json { "dir_path": { "type": "string", "description": "Path to the directory containing GNN files to be type-checked." }, "recursive": { "type": "boolean", "description": "Search directory recursively. Defaults to False.", "optional": true }, "output_dir_base": { "type": "string", "description": "Optional base directory to save the report and other artifacts (HTML, JSON).", "optional": true }, "report_md_filename": { "type": "string", "description": "Optional filename for the markdown report (e.g., 'my_report.md'). Defaults to 'type_check_report.md'.", "optional": true } }
def type_check_gnn_directory_mcp(dir_path, recursive, output_dir_base, report_md_filename) (AST parsed) - *"Run the GNN type checker on all GNN files in a directory. Exposed via MCP."
def type_check_gnn_file(file_path) - *Description: "Runs the GNN type checker on a specified GNN model file."
- Schema: json { "file_path": { "type": "string", "description": "Path to the GNN file to be type-checked." } }
def type_check_gnn_file_mcp(file_path) (AST parsed) - *"Run the GNN type checker on a single GNN file. Exposed via MCP."

Module: `ontology` (at `/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/ontology`)

✅ mcp.py Status: Found (13473 bytes)
Exposed Methods & Tools:
def generate_ontology_report_for_file(gnn_file_path, parsed_annotations, validation_results) (AST parsed) - *"Generates a markdown formatted report string for a single GNN file's ontology annotations."
def get_mcp_interface() (AST parsed) - *"Returns the MCP interface for the Ontology module."
def load_defined_ontology_terms(ontology_terms_path, verbose) (AST parsed) - *"Loads defined ontological terms from a JSON file."
def parse_gnn_ontology_section(gnn_file_content, verbose) (AST parsed) - *"Parses the 'ActInfOntologyAnnotation' section from GNN file content."
def validate_annotations(parsed_annotations, defined_terms, verbose) (AST parsed) - *"Validates parsed GNN annotations against a set of defined ontological terms."

Module: `setup` (at `/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/setup`)

✅ mcp.py Status: Found (4257 bytes)
Exposed Methods & Tools:
def ensure_directory_exists(directory_path) - *Description: "Ensures a directory exists, creating it if necessary. Returns the absolute path."
- Schema: json { "directory_path": { "type": "string", "description": "Path of the directory to create if it doesn't exist." } }
def ensure_directory_exists_mcp(directory_path) (AST parsed) - *"Ensure a directory exists, creating it if necessary. Exposed via MCP."
def find_project_gnn_files(search_directory, recursive) - *Description: "Finds all GNN (.md) files in a specified directory within the project."
- Schema: json { "search_directory": { "type": "string", "description": "The directory to search for GNN (.md) files." }, "recursive": { "type": "boolean", "description": "Set to true to search recursively. Defaults to false.", "optional": true } }
def find_project_gnn_files_mcp(search_directory, recursive) (AST parsed) - *"Find all GNN (.md) files in a directory. Exposed via MCP."
def get_standard_output_paths(base_output_directory) - *Description: "Gets a dictionary of standard output directory paths (e.g., for type_check, visualization), creating them if needed."
- Schema: json { "base_output_directory": { "type": "string", "description": "The base directory where output subdirectories will be managed." } }
def get_standard_output_paths_mcp(base_output_directory) (AST parsed) - *"Get standard output paths for the pipeline. Exposed via MCP."
def register_tools(mcp_instance) (AST parsed) - *"Register setup utility tools with the MCP."

Module: `tests` (at `/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/tests`)

✅ mcp.py Status: Found (7083 bytes)
Exposed Methods & Tools:
def get_test_report(uri) (AST parsed) - *"Retrieve a test report by URI."
def register_tools(mcp) (AST parsed) - *"Register test tools with the MCP."
def run_gnn_type_checker(file_path) - *Description: "Run the GNN type checker on a specific file (via test module)."
- Schema: json { "file_path": { "type": "string", "description": "Path to the GNN file to check" } }
def run_gnn_type_checker_on_directory(dir_path, report_file) - *Description: "Run the GNN type checker on all GNN files in a directory (via test module)."
- Schema: json { "dir_path": { "type": "string", "description": "Path to directory containing GNN files" }, "report_file": { "type": "string", "description": "Optional path to save the report" } }
def run_gnn_unit_tests() - *Description: "Run the GNN unit tests and return results."
def run_type_checker_on_directory(dir_path, report_file) (AST parsed) - *"Run the GNN type checker on a directory of files."
def run_type_checker_on_file(file_path) (AST parsed) - *"Run the GNN type checker on a file."
def run_unit_tests() (AST parsed) - *"Run the GNN unit tests."

Module: `visualization` (at `/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/visualization`)

✅ mcp.py Status: Found (5934 bytes)
Exposed Methods & Tools:
def get_visualization_results(uri) (AST parsed) - *"Retrieve visualization results by URI."
def parse_gnn_file(file_path) - *Description: "Parse a GNN file without visualization"
- Schema: json { "file_path": { "type": "string", "description": "Path to the GNN file to parse" } }
def register_tools(mcp) (AST parsed) - *"Register visualization tools with the MCP."
def visualize_directory(dir_path, output_dir) (AST parsed) - *"Visualize all GNN files in a directory through MCP."
def visualize_file(file_path, output_dir) (AST parsed) - *"Visualize a GNN file through MCP."
def visualize_gnn_directory(dir_path, output_dir) - *Description: "Visualize all GNN files in a directory"
- Schema: json { "dir_path": { "type": "string", "description": "Path to directory containing GNN files" }, "output_dir": { "type": "string", "description": "Optional output directory" } }
def visualize_gnn_file(file_path, output_dir) - *Description: "Generate visualizations for a specific GNN file."
- Schema: json { "file_path": { "type": "string", "description": "Path to the GNN file to visualize" }, "output_dir": { "type": "string", "description": "Optional output directory" } }

Module: `llm` (at `/home/trim/Documents/GitHub/GeneralizedNotationNotation/src/llm`)

✅ mcp.py Status: Found (19238 bytes)
Exposed Methods & Tools:
def ensure_llm_tools_registered(mcp_instance_ref) (AST parsed) - *"Ensures that LLM tools are registered with the provided MCP instance."
def explain_gnn_file_content(file_path_str, aspect_to_explain) (AST parsed) - *"Reads a GNN file, sends its content to an LLM, and returns an explanation."
def generate_professional_summary_from_gnn(file_path_str, experiment_details, target_audience) (AST parsed) - *"Generates a professional summary of a GNN model and its experimental context."
def initialize_llm_module(mcp_instance_ref) (AST parsed) - *"Initializes the LLM module, loads API key, and updates MCP status."
def llm.explain_gnn_file(file_path_str, aspect_to_explain) - *Description: "Reads a GNN specification file and uses an LLM to generate an explanation of its content. Can focus on a specific aspect if provided."
- Schema: json { "type": "object", "properties": { "file_path_str": { "type": "string", "description": "The absolute or relative path to the GNN file." }, "aspect_to_explain": { "type": "string", "description": "(Optional) A specific part or concept within the GNN to focus the explanation on." } }, "required": [ "file_path_str" ] }
def llm.generate_professional_summary(file_path_str, experiment_details, target_audience) - *Description: "Reads a GNN file and optional experiment details, then uses an LLM to generate a professional summary suitable for reports or papers."
- Schema: json { "type": "object", "properties": { "file_path_str": { "type": "string", "description": "The absolute or relative path to the GNN file." }, "experiment_details": { "type": "string", "description": "(Optional) Text describing the experiments conducted with the model, including setup, results, or observations." }, "target_audience": { "type": "string", "description": "(Optional) The intended audience for the summary (e.g., 'fellow researchers', 'project managers'). Default: 'fellow researchers'." } }, "required": [ "file_path_str" ] }
def llm.summarize_gnn_file(file_path_str, user_prompt_suffix) - *Description: "Reads a GNN specification file and uses an LLM to generate a concise summary of its content. Optionally, a user prompt suffix can refine the summary focus."
- Schema: json { "type": "object", "properties": { "file_path_str": { "type": "string", "description": "The absolute or relative path to the GNN file (.md, .gnn.md, .json)." }, "user_prompt_suffix": { "type": "string", "description": "(Optional) Additional instructions or focus points for the summary." } }, "required": [ "file_path_str" ] }
def register_tools(mcp_instance_ref) (AST parsed)
def summarize_gnn_file_content(file_path_str, user_prompt_suffix) (AST parsed) - *"Reads a GNN file, sends its content to an LLM, and returns a summary."

📊 Overall Module Integration Summary

Modules Checked: 8
mcp.py Integrations Found: 8/8
Status: All expected functional modules appear to have an mcp.py integration file. Please ensure each functional module that should be exposed via MCP has its own mcp.py following the project's MCP architecture.

Ontology Processing (Step 8)

🧬 GNN Ontological Annotations Report

📊 Summary of Ontology Processing

Files Processed: 2 / 2
Total Ontological Annotations Found: 32
Total Annotations Validated: 32
✅ Passed: 20
❌ Failed: 12

��️ Report Generated: 2025-06-06 13:41:46 🎯 GNN Source Directory: src/gnn/examples 📖 Ontology Terms Definition: src/ontology/act_inf_ontology_terms.json (Loaded: 48 terms)

Ontological Annotations for `src/gnn/examples/pymdp_pomdp_agent.md`

Mappings:

A_m0 -> LikelihoodMatrixModality0
A_m1 -> LikelihoodMatrixModality1
A_m2 -> LikelihoodMatrixModality2
B_f0 -> TransitionMatrixFactor0
B_f1 -> TransitionMatrixFactor1
C_m0 -> LogPreferenceVectorModality0
C_m1 -> LogPreferenceVectorModality1
C_m2 -> LogPreferenceVectorModality2
D_f0 -> PriorOverHiddenStatesFactor0
D_f1 -> PriorOverHiddenStatesFactor1
s_f0 -> HiddenStateFactor0
s_f1 -> HiddenStateFactor1
s_prime_f0 -> NextHiddenStateFactor0
s_prime_f1 -> NextHiddenStateFactor1
o_m0 -> ObservationModality0
o_m1 -> ObservationModality1
o_m2 -> ObservationModality2
π_f1 -> PolicyVectorFactor1
u_f1 -> ActionFactor1
G -> ExpectedFreeEnergy

Validation Summary: All ontological terms are recognized.

Ontological Annotations for `src/gnn/examples/rxinfer_multiagent_gnn.md`

Mappings:

dt -> TimeStep (INVALID TERM)
gamma -> ConstraintParameter (INVALID TERM)
nr_steps -> TrajectoryLength (INVALID TERM)
nr_iterations -> InferenceIterations (INVALID TERM)
nr_agents -> NumberOfAgents (INVALID TERM)
softmin_temperature -> SoftminTemperature (INVALID TERM)
A -> StateTransitionMatrix (INVALID TERM)
B -> ControlInputMatrix (INVALID TERM)
C -> ObservationMatrix (INVALID TERM)
initial_state_variance -> InitialStateVariance (INVALID TERM)
control_variance -> ControlVariance (INVALID TERM)
goal_constraint_variance -> GoalConstraintVariance (INVALID TERM)

Validation Summary: 12 unrecognized ontological term(s) found.

Rendered Simulators (Step 9)

LLM Processing Outputs (Step 11)

LLM Outputs for pymdp_pomdp_agent: pymdp_pomdp_agent

JSON Files

pymdp_pomdp_agent_comprehensive_analysis.json

{
  "model_purpose": "The model represents a Multifactor PyMDP agent capable of handling multiple observation modalities and hidden state factors, designed for decision-making and inference in uncertain environments using Active Inference.",
  "key_components": {
    "observation_modalities": {
      "state_observation": {
        "outcomes": 3
      },
      "reward": {
        "outcomes": 3
      },
      "decision_proprioceptive": {
        "outcomes": 3
      }
    },
    "hidden_state_factors": {
      "reward_level": {
        "states": 2
      },
      "decision_state": {
        "states": 3
      }
    },
    "actions": {
      "decision_state": {
        "controllable": true,
        "actions": 3
      }
    },
    "parameters": {
      "A_matrices": [
        "A_m0",
        "A_m1",
        "A_m2"
      ],
      "B_matrices": [
        "B_f0",
        "B_f1"
      ],
      "C_vectors": [
        "C_m0",
        "C_m1",
        "C_m2"
      ],
      "D_vectors": [
        "D_f0",
        "D_f1"
      ],
      "hidden_states": [
        "s_f0",
        "s_f1",
        "s_prime_f0",
        "s_prime_f1"
      ],
      "observations": [
        "o_m0",
        "o_m1",
        "o_m2"
      ],
      "policies": [
        "\u03c0_f1"
      ],
      "actions_taken": [
        "u_f1"
      ],
      "expected_free_energy": [
        "G"
      ],
      "time_step": [
        "t"
      ]
    }
  },
  "component_interactions": {
    "hidden_states": {
      "D_f0, D_f1": "influence hidden states s_f0 and s_f1",
      "s_f0, s_f1": "affect A_m0, A_m1, A_m2",
      "A_m0, A_m1, A_m2": "produce observations o_m0, o_m1, o_m2",
      "s_f0, s_f1, u_f1": "impact B_f0 and B_f1 (u_f1 mainly affects B_f1)",
      "B_f0, B_f1": "determine next hidden states s_prime_f0 and s_prime_f1",
      "C_m0, C_m1, C_m2": "contribute to G (Expected Free Energy)",
      "G": "guides policy \u03c0_f1",
      "\u03c0_f1": "determines action u_f1",
      "G=ExpectedFreeEnergy": "provides overall assessment of expected outcomes"
    }
  },
  "data_types_and_dimensions": {
    "A_matrices": {
      "A_m0": {
        "type": "float",
        "dimensions": [
          3,
          2,
          3
        ]
      },
      "A_m1": {
        "type": "float",
        "dimensions": [
          3,
          2,
          3
        ]
      },
      "A_m2": {
        "type": "float",
        "dimensions": [
          3,
          2,
          3
        ]
      }
    },
    "B_matrices": {
      "B_f0": {
        "type": "float",
        "dimensions": [
          2,
          2,
          1
        ]
      },
      "B_f1": {
        "type": "float",
        "dimensions": [
          3,
          3,
          3
        ]
      }
    },
    "C_vectors": {
      "C_m0": {
        "type": "float",
        "dimensions": [
          3
        ]
      },
      "C_m1": {
        "type": "float",
        "dimensions": [
          3
        ]
      },
      "C_m2": {
        "type": "float",
        "dimensions": [
          3
        ]
      }
    },
    "D_vectors": {
      "D_f0": {
        "type": "float",
        "dimensions": [
          2
        ]
      },
      "D_f1": {
        "type": "float",
        "dimensions": [
          3
        ]
      }
    },
    "hidden_states": {
      "s_f0": {
        "type": "float",
        "dimensions": [
          2,
          1
        ]
      },
      "s_f1": {
        "type": "float",
        "dimensions": [
          3,
          1
        ]
      },
      "s_prime_f0": {
        "type": "float",
        "dimensions": [
          2,
          1
        ]
      },
      "s_prime_f1": {
        "type": "float",
        "dimensions": [
          3,
          1
        ]
      }
    },
    "observations": {
      "o_m0": {
        "type": "float",
        "dimensions": [
          3,
          1
        ]
      },
      "o_m1": {
        "type": "float",
        "dimensions": [
          3,
          1
        ]
      },
      "o_m2": {
        "type": "float",
        "dimensions": [
          3,
          1
        ]
      }
    },
    "policies": {
      "\u03c0_f1": {
        "type": "float",
        "dimensions": [
          3
        ]
      },
      "u_f1": {
        "type": "int",
        "dimensions": [
          1
        ]
      },
      "G": {
        "type": "float",
        "dimensions": [
          1
        ]
      },
      "t": {
        "type": "int",
        "dimensions": [
          1
        ]
      }
    }
  },
  "potential_applications": [
    "Developing intelligent agents that adapt to dynamic environments.",
    "Robotics where multi-modal sensory inputs are crucial for decision making.",
    "Game AI that requires complex state and reward management.",
    "Research in cognitive science concerning decision-making processes."
  ],
  "limitations_or_ambiguities": [
    "The model assumes that control of the decision state can be accurately represented by discrete actions, which may not capture continuous control dynamics.",
    "The impact of uncontrolled factors (like B_f0) may not be fully understood or utilized in practical scenarios.",
    "The model's unbounded time horizon may not be suitable for all applications, especially those requiring finite planning."
  ],
  "ontology_mapping_assessment": {
    "present_terms": [
      "LikelihoodMatrixModality0",
      "LikelihoodMatrixModality1",
      "LikelihoodMatrixModality2",
      "TransitionMatrixFactor0",
      "TransitionMatrixFactor1",
      "LogPreferenceVectorModality0",
      "LogPreferenceVectorModality1",
      "LogPreferenceVectorModality2",
      "PriorOverHiddenStatesFactor0",
      "PriorOverHiddenStatesFactor1",
      "HiddenStateFactor0",
      "HiddenStateFactor1",
      "NextHiddenStateFactor0",
      "NextHiddenStateFactor1",
      "ObservationModality0",
      "ObservationModality1",
      "ObservationModality2",
      "PolicyVectorFactor1",
      "ActionFactor1",
      "ExpectedFreeEnergy"
    ],
    "relevance": "The terms accurately reflect the components and interactions detailed in the GNN model, providing a solid framework for understanding the agent's structure and functionality."
  }
}

pymdp_pomdp_agent_comprehensive_analysis.json

pymdp_pomdp_agent_qa.json

[
  {
    "question": "What are the implications of having multiple observation modalities in the PyMDP agent, and how might they interact to influence decision making?",
    "answer": "The GNN file indicates that the PyMDP agent has multiple observation modalities\u2014specifically, \"state_observation,\" \"reward,\" and \"decision_proprioceptive,\" each with distinct outcomes. The implications of having these multiple modalities include:\n\n1. **Rich Information Intake**: Each modality can provide different types of information, allowing the agent to build a more comprehensive understanding of its environment and internal states.\n\n2. **Enhanced State Inference**: The agent can leverage the diverse observations to improve its state inference process. Different modalities can corroborate or contradict each other, leading to more accurate estimations of the hidden states.\n\n3. **Policy Adaptation**: The preferences defined in the C vectors for different modalities can influence the decision-making process. The agent can adjust its policies based on the feedback from various modalities, leading to more nuanced decision-making.\n\n4. **Inter-Modal Interaction**: The connections detailed in the GNN specify how hidden states are influenced by observations, suggesting that changes in one modality can affect the perception and interpretation of others. For instance, a high reward observation might lead to a different decision state than a neutral one, modifying the actions sampled.\n\nOverall, the interaction between these modalities can create a more adaptive and responsive agent, capable of making informed decisions based on a broader set of inputs."
  },
  {
    "question": "How does the controllable 'decision_state' factor affect the overall dynamics of the agent, particularly in relation to the uncontrolled 'reward_level' factor?",
    "answer": "The GNN file indicates that the controllable 'decision_state' factor affects the overall dynamics of the agent primarily through its influence on the transition matrix B_f1, which governs the state transitions for 'decision_state' based on the chosen action (u_f1). This factor has three possible actions that can be taken, allowing the agent to control its decision-making process.\n\nIn contrast, the 'reward_level' factor is uncontrolled, meaning its transitions are governed solely by the matrix B_f0, which does not depend on any actions. The dynamics of the 'reward_level' factor are thus independent of the 'decision_state' factor's actions, as it transitions according to its own likelihoods, effectively remaining constant regardless of decisions made by the agent. \n\nTherefore, while the 'decision_state' can be manipulated to affect the agent's behavior and decision-making, it does not directly influence the transitions of the 'reward_level' factor, which operates autonomously within the agent's dynamics."
  },
  {
    "question": "What are the potential impacts of the chosen prior distributions (D_f0 and D_f1) on the inference processes and the agent's learning capabilities?",
    "answer": "The GNN file specifies uniform prior distributions for both hidden state factors: D_f0 for \"reward_level\" (2 states) and D_f1 for \"decision_state\" (3 states). The impacts of these chosen priors on the inference processes and the agent's learning capabilities are as follows:\n\n1. **Inference Processes**: \n   - Uniform priors suggest that the agent starts with no bias towards any particular state, which can lead to slower convergence during state inference as the agent may require more observations to update its beliefs about the hidden states effectively.\n   - Given that D_f0 and D_f1 are uniformly distributed, the initial state estimates may be less informative, potentially complicating the inference of both states, particularly in situations where one state is more likely than others.\n\n2. **Learning Capabilities**:\n   - The uniform prior may hinder the agent\u2019s ability to learn quickly from experiences, especially if the actual distributions of the hidden states are skewed. This can lead to suboptimal decision-making in early stages of learning.\n   - Over time, as the agent accumulates more observations, it can adjust its beliefs, but starting with a uniform prior may require a longer learning phase to achieve optimal performance compared to more informative priors.\n\nIn summary, while uniform priors allow for a non-biased starting point, they may negatively affect the efficiency and speed of the agent's learning and inference processes, particularly in the early stages."
  },
  {
    "question": "How do the specified transition matrices (B_f0 and B_f1) reflect the underlying assumptions about state transitions, and what real-world scenarios could they model effectively?",
    "answer": "The transition matrices (B_f0 and B_f1) in the provided GNN file reflect distinct underlying assumptions about state transitions in a multifactor PyMDP agent.\n\n1. **B_f0 (Transition Matrix for \"reward_level\")**: \n   - This matrix is defined for an uncontrolled factor with 2 states. It is an identity matrix, meaning that the state transitions are deterministic; if the agent is in a given state, it will remain in that state regardless of any action (since there is only one implicit action). This reflects an assumption that the \"reward_level\" does not change based on external factors or decisions, which could model scenarios where a resource level remains stable unless externally influenced.\n\n2. **B_f1 (Transition Matrix for \"decision_state\")**:\n   - This matrix allows transitions between 3 states based on 3 possible actions. Each row represents the next state given the current state and the action taken, indicating that the decision-making process is influenced by the agent's actions. This reflects an assumption that the agent can actively change its decision state through its actions. This transition model could effectively represent scenarios such as a robot navigating a maze where its decisions (turn left, turn right, move forward) lead to different outcomes in its pathfinding.\n\nOverall, B_f0 could model stable environments where conditions do not change, while B_f1 is suited for dynamic scenarios where the agent's actions lead to varied outcomes."
  },
  {
    "question": "In what ways might the expected free energy (G) serve as a guiding principle for the agent's actions, and how could variations in the preference vectors (C_m0, C_m1, C_m2) alter its behavior?",
    "answer": "The expected free energy (G) serves as a guiding principle for the agent's actions by functioning as a scalar value that the agent aims to minimize. In the context of the GNN representation, G is influenced by the preferences encoded in the preference vectors (C_m0, C_m1, C_m2) and the observations made by the agent. The relationship indicated by the equations suggests that by adjusting these preference vectors, the agent can effectively guide its policy (\u03c0_f1) towards actions that are expected to result in lower free energy.\n\nVariations in the preference vectors can alter the agent's behavior significantly. For instance, if the preferences (C_m0, C_m1, C_m2) reflect higher values for certain observations or outcomes, the agent may prioritize actions that align with those preferences, potentially increasing the likelihood of selecting actions that yield higher rewards or desirable states. Conversely, negative preferences (e.g., C_m1 having a value of -2.0) can lead the agent to avoid certain actions or states, thus shaping its decision-making process based on the desirability of the outcomes.\n\nIn summary, G acts as a measure for action selection based on minimizing expected free energy, while the preference vectors directly influence the agent's priorities and decision-making behavior."
  }
]

pymdp_pomdp_agent_qa.json

Text/Log Files

pymdp_pomdp_agent_summary.txt

### Summary of the GNN Model: Multifactor PyMDP Agent

**Model Name:** Multifactor PyMDP Agent v1

**Purpose:**  
The model represents a PyMDP agent designed for active inference, incorporating multiple observation modalities and hidden state factors. It aims to facilitate decision-making processes by integrating various forms of observations and controlling hidden state dynamics.

**Key Components:**

1. **Observation Modalities:**
   - **State Observation:** 3 possible outcomes.
   - **Reward:** 3 possible outcomes.
   - **Decision Proprioceptive:** 3 possible outcomes.

2. **Hidden State Factors:**
   - **Reward Level:** 2 states (e.g., low/high reward).
   - **Decision State:** 3 states (e.g., decision-making scenarios).

3. **Control Mechanism:**
   - The decision state factor is controllable with 3 possible actions, impacting the dynamics of the agent's decision-making process.

**Main Connections:**
- The model interrelates various components through specific connections:
  - The priors (D_f0, D_f1) influence the hidden states (s_f0, s_f1).
  - Hidden states affect the likelihood matrices (A_m0, A_m1, A_m2) for different modalities.
  - Observations (o_m0, o_m1, o_m2) emerge from the likelihood matrices.
  - Actions taken (u_f1) influence transitions for the decision state (B_f1) while the reward level transitions (B_f0) remain uncontrolled.
  - The model computes expected free energy (G) from the preferences (C_m0, C_m1, C_m2) and informs the policy distribution over actions (π_f1).

Overall, this model encapsulates the dynamics of a multifactor PyMDP agent, aiming to leverage observations and control mechanisms for effective decision-making in uncertain environments.

pymdp_pomdp_agent_summary.txt

LLM Outputs for rxinfer_multiagent_gnn: rxinfer_multiagent_gnn

Text/Log Files

rxinfer_multiagent_gnn_comprehensive_analysis.txt

```json
{
  "model_purpose": "The model is designed for multi-agent trajectory planning in a 2D environment, incorporating obstacle avoidance, goal-directed behavior, and inter-agent collision avoidance. It aims to simulate and optimize the trajectories of multiple agents while considering their interactions with obstacles and each other.",
  "key_components": {
    "states": {
      "description": "The state of each agent in the system, represented in a 4-dimensional state space.",
      "dimensionality": 4
    },
    "observations": {
      "description": "The observations are derived from the state of the agents and are used to infer their positions over time.",
      "dimensionality": 2
    },
    "actions": {
      "description": "Control inputs that influence the agents' movements within the environment.",
      "dimensionality": 2
    },
    "parameters": {
      "description": "Various model parameters include time step, constraint parameters, and variances for initial state, control, and goal constraints.",
      "types": [
        "float",
        "int",
        "bool",
        "string"
      ]
    },
    "agents": {
      "description": "Each agent has a unique ID, radius, initial position, and target position.",
      "number_of_agents": 4
    },
    "obstacles": {
      "description": "Defined obstacles in the environment, including door and wall obstacles, with specified centers and sizes."
    }
  },
  "component_interactions": {
    "state_space_model": {
      "inputs": ["dt", "A", "B", "C"],
      "outputs": ["agent_trajectories"]
    },
    "goal_directed_behavior": {
      "inputs": ["agent_trajectories", "goal_constraint_variance"],
      "outputs": []
    },
    "obstacle_avoidance": {
      "inputs": ["agent_trajectories", "gamma", "gamma_shape", "gamma_scale_factor"],
      "outputs": []
    },
    "collision_avoidance": {
      "inputs": ["agent_trajectories", "nr_agents"],
      "outputs": []
    },
    "planning_system": {
      "inputs": ["goal_directed_behavior", "obstacle_avoidance", "collision_avoidance"],
      "outputs": []
    }
  },
  "data_types_and_dimensions": {
    "dt": {
      "type": "float",
      "dimension": 1
    },
    "gamma": {
      "type": "float",
      "dimension": 1
    },
    "nr_steps": {
      "type": "int",
      "dimension": 1
    },
    "nr_iterations": {
      "type": "int",
      "dimension": 1
    },
    "nr_agents": {
      "type": "int",
      "dimension": 1
    },
    "A": {
      "type": "float",
      "dimension": [4, 4]
    },
    "B": {
      "type": "float",
      "dimension": [4, 2]
    },
    "C": {
      "type": "float",
      "dimension": [2, 4]
    },
    "initial_state_variance": {
      "type": "float",
      "dimension": 1
    },
    "control_variance": {
      "type": "float",
      "dimension": 1
    },
    "goal_constraint_variance": {
      "type": "float",
      "dimension": 1
    },

... (file truncated, total lines: 141)

rxinfer_multiagent_gnn_comprehensive_analysis.txt

rxinfer_multiagent_gnn_summary.txt

### Summary of the GNN Model: Multi-agent Trajectory Planning

**Model Name:** Multi-agent Trajectory Planning

**Purpose:** 
The model is designed for simulating and planning the trajectories of multiple agents in a 2D environment while accounting for obstacles, goal-directed behaviors, and inter-agent collision avoidance. It leverages the RxInfer.jl framework to facilitate inference and trajectory planning.

**Key Components:**

1. **State Space Model:**
   - **Parameters:**
     - `dt`: Time step for the model.
     - `gamma`: Constraint parameter for obstacle avoidance.
     - `nr_steps`: Number of time steps in the simulation.
     - `nr_iterations`: Number of inference iterations.
     - `nr_agents`: Number of agents involved.
   - **Matrices:**
     - `A`: State transition matrix defining how agents evolve over time.
     - `B`: Control input matrix influencing the state based on inputs.
     - `C`: Observation matrix mapping states to observed variables.

2. **Prior Distributions:**
   - Variances for initial states, control inputs, and goal constraints, along with parameters for Gamma distributions for uncertainty modeling.

3. **Visualization Parameters:**
   - Settings for plotting trajectories, including boundaries, resolution, and visual properties of agents.

4. **Environment Definitions:**
   - Specifications for obstacles in various configurations (door, wall, combined) with centers and sizes defined.

5. **Agent Configurations:**
   - Details for four agents, including their IDs, radii, initial and target positions.

6. **Experiment Configurations:**
   - Random seeds for reproducibility, results directory, and filenames for visualizations of control signals and obstacles.

**Main Connections:**
- The model establishes connections between parameters and computations, such as:
  - State evolution using the state space model and variances for initial states and controls.
  - Goal-directed behavior derived from agent trajectories and goal constraints.
  - Obstacle avoidance and collision avoidance mechanisms based on agent positions and defined constraints.
  - Integration into a complete planning system that synthesizes these components for trajectory planning.

This model provides a comprehensive framework for simulating multi-agent environments with complex interactions and constraints, ensuring effective trajectory planning while avoiding obstacles and collisions.

rxinfer_multiagent_gnn_summary.txt

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2025-06-06 13:41:20,386 - GNN_Pipeline - DEBUG -     [STDOUT] # GNN Example: Multifactor PyMDP Agent
2025-06-06 13:41:20,386 - GNN_Pipeline - DEBUG -     [STDOUT] # Format: Markdown representation of a Multifactor PyMDP model in Active Inference format
2025-06-06 13:41:20,386 - GNN_Pipeline - DEBUG -     [STDOUT] # Version: 1.0
2025-06-06 13:41:20,386 - GNN_Pipeline - DEBUG -     [STDOUT] # This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.
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2025-06-06 13:41:20,386 - GNN_Pipeline - DEBUG -     [STDOUT] ## GNNSection
2025-06-06 13:41:20,386 - GNN_Pipeline - DEBUG -     [STDOUT] MultifactorPyMDPAgent
2025-06-06 13:41:20,386 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,386 - GNN_Pipeline - DEBUG -     [STDOUT] ## GNNVersionAndFlags
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2025-06-06 13:41:20,386 - GNN_Pipeline - DEBUG -     [STDOUT] ## ModelName
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] Multifactor PyMDP Agent v1
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] ## ModelAnnotation
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] This model represents a PyMDP agent with multiple observation modalities and hidden state fac
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2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] # GNN Example: Multifactor PyMDP Agent
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] # Format: Markdown representation of a Multifactor PyMDP model in Active Inference format
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] # Version: 1.0
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] # This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.
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2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] ## GNNSection
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] MultifactorPyMDPAgent
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] ## GNNVersionAndFlags
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] GNN v1
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] ## ModelName
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] Multifactor PyMDP Agent v1
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] ## ModelAnnotation
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] This model represents a PyMDP agent with multiple observation modalities and hidden state fac
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,372 - __main__ - DEBUG -   Found ModelParameters section in src/gnn/examples/pymdp_pomdp_agent.md
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,375 - __main__ - DEBUG -     Parsed ModelParameter: num_hidden_states_factors = [2, 3]
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,375 - __main__ - DEBUG -     Parsed ModelParameter: num_obs_modalities = [3, 3, 3]
2025-06-06 13:41:20,387 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,375 - __main__ - DEBUG -     Parsed ModelParameter: num_control_factors = [1, 3]
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,375 - __main__ - DEBUG - Processing file: src/gnn/examples/rxinfer_multiagent_gnn.md
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,375 - __main__ - DEBUG - Successfully read content from src/gnn/examples/rxinfer_multiagent_gnn.md.
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,375 - __main__ - DEBUG -   Found '## ModelName' header in src/gnn/examples/rxinfer_multiagent_gnn.md
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,375 - __main__ - DEBUG -     Extracted ModelName: 'Multi-agent Trajectory Planning' from src/gnn/examples/rxinfer_multiagent_gnn.md
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,375 - __main__ - DEBUG -   StateSpaceBlock section not found in src/gnn/examples/rxinfer_multiagent_gnn.md
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,375 - __main__ - DEBUG -     Content snippet for src/gnn/examples/rxinfer_multiagent_gnn.md (up to 500 chars) where StateSpaceBlock was not found:
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] # GNN Example: RxInfer Multi-agent Trajectory Planning
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] # Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] # Version: 1.0
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] # This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] ## GNNSection
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] RxInferMultiAgentTrajectoryPlanning
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] ## GNNVersionAndFlags
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] GNN v1
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] ## ModelName
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] Multi-agent Trajectory Planning
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] ## ModelAnnotation
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] This model represents a multi-agent trajectory planning scenario i
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,375 - __main__ - DEBUG -   Connections section not found in src/gnn/examples/rxinfer_multiagent_gnn.md
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,375 - __main__ - DEBUG -     Content snippet for src/gnn/examples/rxinfer_multiagent_gnn.md (up to 500 chars) where Connections was not found:
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] # GNN Example: RxInfer Multi-agent Trajectory Planning
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] # Format: Markdown representation of a Multi-agent Trajectory Planning model for RxInfer.jl
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] # Version: 1.0
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] # This file is machine-readable and represents the configuration for the RxInfer.jl multi-agent trajectory planning example.
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] ## GNNSection
2025-06-06 13:41:20,388 - GNN_Pipeline - DEBUG -     [STDOUT] RxInferMultiAgentTrajectoryPlanning
2025-06-06 13:41:20,389 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,389 - GNN_Pipeline - DEBUG -     [STDOUT] ## GNNVersionAndFlags
2025-06-06 13:41:20,389 - GNN_Pipeline - DEBUG -     [STDOUT] GNN v1
2025-06-06 13:41:20,389 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,389 - GNN_Pipeline - DEBUG -     [STDOUT] ## ModelName
2025-06-06 13:41:20,389 - GNN_Pipeline - DEBUG -     [STDOUT] Multi-agent Trajectory Planning
2025-06-06 13:41:20,389 - GNN_Pipeline - DEBUG -     [STDOUT] 
2025-06-06 13:41:20,389 - GNN_Pipeline - DEBUG -     [STDOUT] ## ModelAnnotation
2025-06-06 13:41:20,389 - GNN_Pipeline - DEBUG -     [STDOUT] This model represents a multi-agent trajectory planning scenario i
2025-06-06 13:41:20,389 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,375 - __main__ - DEBUG -   Found ModelParameters section in src/gnn/examples/rxinfer_multiagent_gnn.md
2025-06-06 13:41:20,389 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,376 - __main__ - INFO - GNN discovery report saved to: output/gnn_processing_step/1_gnn_discovery_report.md
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2025-06-06 13:41:20,389 - GNN_Pipeline - DEBUG - --- End of 1_gnn.py output ---
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2025-06-06 13:41:20,389 - GNN_Pipeline - INFO - ⏳ Setting up environment and dependencies (timeout: 1200s). This may take several minutes...
2025-06-06 13:41:22,865 - GNN_Pipeline - DEBUG - --- Output from 2_setup.py (stdout) ---
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2025-06-06 13:41:22,866 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,438 - __main__ - INFO - --- Environment Information ---
2025-06-06 13:41:22,866 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,438 - __main__ - INFO - Python version: 3.12.2 | packaged by conda-forge | (main, Feb 16 2024, 20:50:58) [GCC 12.3.0]
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2025-06-06 13:41:22,866 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,636 - __main__ - DEBUG -   Found 0 GNN .md files (recursively in target: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/gnn/examples)
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2025-06-06 13:41:22,866 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,637 - __main__ - INFO -   ⏳ This may take a few minutes, especially if installing dependencies...
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2025-06-06 13:41:22,867 - GNN_Pipeline - DEBUG -     [STDOUT] 2025-06-06 13:41:20,637 - INFO - 📁 Project root: /home/trim/Documents/GitHub/GeneralizedNotationNotation/src
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2025-06-06 13:41:22,867 - GNN_Pipeline - DEBUG -     [STDOUT] pip 25.1.1 from /home/trim/Documents/GitHub/GeneralizedNotationNotation/src/.venv/lib/python3.12/site-packages/pip (python 3.12)
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pipeline.log

Table of Contents

GNN Discovery (Step 1)

GNN File Discovery Report

Overall Summary

Detailed File Analysis

File: src/gnn/examples/pymdp_pomdp_agent.md

Found Sections:

File: src/gnn/examples/rxinfer_multiagent_gnn.md

Found Sections:

Test Reports (Step 3)

GNN Type Checker (Step 4)

Type Check Report

GNN Type Checker Report

pymdp_pomdp_agent.md: ✅ VALID

rxinfer_multiagent_gnn.md: ✅ VALID

Resource Estimates: resource_estimates

Images

Markdown Reports

resource_report.md

GNN Resource Estimation Report

pymdp_pomdp_agent.md

Model Info

Complexity Metrics

rxinfer_multiagent_gnn.md

Model Info

Complexity Metrics

Metric Definitions

General Metrics

Complexity Metrics (scores are generally relative; higher often means more complex)

HTML Reports/Outputs

resource_report_detailed.html

JSON Files

resource_data.json

GNN Exports (Step 5)

Export Step Report

📤 GNN Export Step Summary

⚙️ Configuration

📊 Export Statistics

Exports for pymdp_pomdp_agent: pymdp_pomdp_agent

JSON Files

pymdp_pomdp_agent.json

Text/Log Files

pymdp_pomdp_agent.txt

Other Files

Exports for rxinfer_multiagent_gnn: rxinfer_multiagent_gnn

JSON Files

rxinfer_multiagent_gnn.json

Text/Log Files

rxinfer_multiagent_gnn.txt

Other Files

GNN Processing Summary (Overall File List)

📊 GNN Processing Summary

⚙️ Processing Configuration

📁 GNN Files Discovered

🔄 Pipeline Execution Status

📊 Output Summary

🔍 Key Findings

📋 Recommendations

General Improvements

GNN Visualizations (Step 6)

Visualizations for pymdp_pomdp_agent: pymdp_pomdp_agent

Images

Markdown Reports

file_content.md

GNN File: src/gnn/examples/pymdp_pomdp_agent.md\n\n## Raw File Content\n\n```\n# GNN Example: Multifactor PyMDP Agent

Format: Markdown representation of a Multifactor PyMDP model in Active Inference format

Version: 1.0

This file is machine-readable and attempts to represent a PyMDP agent with multiple observation modalities and hidden state factors.

GNNSection

GNNVersionAndFlags

ModelName

ModelAnnotation

StateSpaceBlock

A_matrices are defined per modality: A_m[observation_outcomes, state_factor0_states, state_factor1_states]

B_matrices are defined per hidden state factor: B_f[states_next, states_previous, actions]

C_vectors are defined per modality: C_m[observation_outcomes]

D_vectors are defined per hidden state factor: D_f[states]

Hidden States

Observations

Policy and Control

File: `src/gnn/examples/pymdp_pomdp_agent.md`

File: `src/gnn/examples/rxinfer_multiagent_gnn.md`